---------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/sungkim/Dropbox/KimPelc5/Vaccination_paper/Final_PB/Replication/vaccine_main.log
  log type:  text
 opened on:   7 Jan 2024, 16:24:31

. do "/var/folders/bc/1b3ytrvs72l4kkkjpggfz82r0000gn/T//SD41615.000000"

. use "KimPelc_vaccine_main.dta", clear

. 
. * Table 1. The Effects of Ideological Distance on COVID-19 Vaccination Decision
. 
. eststo clear

. 
. eststo: regress vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share female age covid_diag
> nosed hs college income_50k income_100k city suburb town white black race_hispanic asian , cluster(inpu
> tstate)

Linear regression                               Number of obs     =     21,697
                                                F(18, 48)         =     118.17
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1853
                                                Root MSE          =     .38435

                                 (Std. Err. adjusted for 49 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0226323   .0024383    -9.28   0.000    -.0275349   -.0177296
            ideo7 |   -.069316   .0021135   -32.80   0.000    -.0735655   -.0650666
st_ideo7_mean2021 |   -.046985   .0125037    -3.76   0.000    -.0721254   -.0218446
     cz_imm_share |   .0823679   .0351855     2.34   0.023     .0116227     .153113
           female |   -.030404   .0062041    -4.90   0.000    -.0428781   -.0179299
              age |   .0051087   .0002495    20.47   0.000      .004607    .0056104
  covid_diagnosed |   .0768954   .0082668     9.30   0.000     .0602739    .0935169
               hs |   .1330115   .0194939     6.82   0.000     .0938164    .1722066
          college |   .2164688   .0183971    11.77   0.000     .1794789    .2534586
       income_50k |   .0547775   .0071715     7.64   0.000     .0403583    .0691967
      income_100k |   .0677417   .0074006     9.15   0.000     .0528618    .0826216
             city |   .0765015   .0090236     8.48   0.000     .0583583    .0946447
           suburb |   .0778485   .0077645    10.03   0.000      .062237    .0934601
             town |   .0388647   .0101242     3.84   0.000     .0185086    .0592209
            white |   .0854653   .0143199     5.97   0.000     .0566732    .1142574
            black |    .040826   .0150122     2.72   0.009     .0106419      .07101
    race_hispanic |   .0864686   .0164022     5.27   0.000     .0534898    .1194474
            asian |   .1749528   .0215494     8.12   0.000     .1316249    .2182808
            _cons |   .6115975   .0554289    11.03   0.000     .5001503    .7230448
-----------------------------------------------------------------------------------
(est1 stored)

. eststo: areg  vaccinated st_ideo7_dist2021 ideo7 cz_imm_share female age covid_diagnosed hs college inc
> ome_50k income_100k city suburb town white black race_hispanic asian , cluster(inputstate) absorb(input
> state)

Linear regression, absorbing indicators         Number of obs     =     21,697
Absorbed variable: inputstate                   No. of categories =         49
                                                F(  17,     48)   =     105.40
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1884
                                                Adj R-squared     =     0.1860
                                                Root MSE          =     0.3840

                                 (Std. Err. adjusted for 49 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0223415   .0024129    -9.26   0.000    -.0271931     -.01749
            ideo7 |  -.0693299    .002113   -32.81   0.000    -.0735784   -.0650813
     cz_imm_share |   .1479865   .0405829     3.65   0.001      .066389    .2295839
           female |  -.0302977   .0062495    -4.85   0.000    -.0428631   -.0177323
              age |   .0051642   .0002502    20.64   0.000     .0046612    .0056673
  covid_diagnosed |   .0771976   .0082214     9.39   0.000     .0606673    .0937279
               hs |   .1328568   .0192734     6.89   0.000      .094105    .1716086
          college |   .2144685   .0184233    11.64   0.000     .1774259     .251511
       income_50k |   .0548872   .0072607     7.56   0.000     .0402885    .0694858
      income_100k |   .0659056   .0073922     8.92   0.000     .0510425    .0807687
             city |   .0789625   .0094942     8.32   0.000     .0598732    .0980519
           suburb |   .0782851   .0075975    10.30   0.000     .0630094    .0935609
             town |   .0375066   .0101441     3.70   0.001     .0171105    .0579028
            white |   .0837238   .0145261     5.76   0.000     .0545172    .1129305
            black |   .0337118   .0149771     2.25   0.029     .0035983    .0638253
    race_hispanic |   .0894624   .0176601     5.07   0.000     .0539544    .1249704
            asian |   .1754869   .0215574     8.14   0.000     .1321428     .218831
            _cons |   .4165353   .0358289    11.63   0.000     .3444963    .4885742
-----------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: regress vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share female age covid_diag
> nosed hs college income_50k income_100k city suburb town white black race_hispanic asian , cluster(czon
> e)

Linear regression                               Number of obs     =     21,697
                                                F(18, 621)        =     109.37
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1854
                                                Root MSE          =     .38432

                                     (Std. Err. adjusted for 622 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0208276   .0025753    -8.09   0.000    -.0258849   -.0157703
            ideo7 |  -.0679875   .0021887   -31.06   0.000    -.0722857   -.0636893
cz_ideo7_mean2021 |  -.0400351   .0072449    -5.53   0.000    -.0542625   -.0258077
     cz_imm_share |   .0722064    .033952     2.13   0.034     .0055317    .1388811
           female |  -.0303093    .005403    -5.61   0.000    -.0409196   -.0196989
              age |   .0050939   .0002141    23.79   0.000     .0046734    .0055144
  covid_diagnosed |   .0764716    .007056    10.84   0.000     .0626151    .0903281
               hs |   .1336186   .0189006     7.07   0.000     .0965017    .1707355
          college |   .2152585   .0186681    11.53   0.000     .1785983    .2519188
       income_50k |   .0540582   .0067431     8.02   0.000     .0408161    .0673002
      income_100k |   .0663964   .0077678     8.55   0.000     .0511422    .0816507
             city |   .0716499   .0097249     7.37   0.000     .0525523    .0907475
           suburb |   .0725634   .0086603     8.38   0.000     .0555564    .0895705
             town |   .0374531   .0106288     3.52   0.000     .0165804    .0583258
            white |   .0854355   .0141606     6.03   0.000     .0576269    .1132441
            black |    .038969   .0152007     2.56   0.011     .0091181    .0688199
    race_hispanic |   .0878205    .016118     5.45   0.000     .0561682    .1194729
            asian |   .1767529   .0197892     8.93   0.000      .137891    .2156148
            _cons |   .5827848   .0404931    14.39   0.000     .5032648    .6623048
-----------------------------------------------------------------------------------
(est3 stored)

. eststo: areg vaccinated cz_ideo7_dist2021 ideo7 female age covid_diagnosed hs college income_50k income
> _100k city suburb town white black race_hispanic asian, cluster(czone) absorb(czone)

Linear regression, absorbing indicators         Number of obs     =     21,697
Absorbed variable: czone                        No. of categories =        622
                                                F(  16,    621)   =      83.22
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2181
                                                Adj R-squared     =     0.1945
                                                Root MSE          =     0.3820

                                     (Std. Err. adjusted for 622 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0205392   .0026364    -7.79   0.000    -.0257164   -.0153619
            ideo7 |  -.0681413    .002221   -30.68   0.000    -.0725028   -.0637798
           female |  -.0291142   .0055301    -5.26   0.000    -.0399742   -.0182541
              age |   .0050596   .0002203    22.97   0.000     .0046271    .0054922
  covid_diagnosed |   .0776691   .0073073    10.63   0.000     .0633191    .0920192
               hs |    .132494   .0193804     6.84   0.000     .0944349    .1705531
          college |   .2116462   .0191211    11.07   0.000     .1740964    .2491961
       income_50k |     .05171    .006877     7.52   0.000     .0382049     .065215
      income_100k |   .0610443   .0079795     7.65   0.000     .0453743    .0767143
             city |   .0671903    .010996     6.11   0.000     .0455965    .0887841
           suburb |   .0656769   .0101226     6.49   0.000     .0457983    .0855555
             town |   .0334275   .0113182     2.95   0.003     .0112009    .0556541
            white |   .0837663   .0148005     5.66   0.000     .0547013    .1128314
            black |   .0306733   .0152714     2.01   0.045     .0006835    .0606632
    race_hispanic |   .0895793   .0167149     5.36   0.000     .0567548    .1224039
            asian |   .1760403   .0202285     8.70   0.000     .1363157    .2157648
            _cons |   .4483367    .028148    15.93   0.000     .3930599    .5036135
-----------------------------------------------------------------------------------
(est4 stored)

. 
. eststo: xtmelogit vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share female age covid_di
> agnosed hs college income_50k income_100k city suburb town white black race_hispanic asian || inputstat
> e: 

Refining starting values: 

Iteration 0:   log likelihood = -9754.7106  (not concave)
Iteration 1:   log likelihood = -9700.1487  
Iteration 2:   log likelihood =  -9699.272  

Performing gradient-based optimization: 

Iteration 0:   log likelihood =  -9699.272  
Iteration 1:   log likelihood = -9698.3821  
Iteration 2:   log likelihood = -9698.3788  
Iteration 3:   log likelihood = -9698.3788  

Mixed-effects logistic regression               Number of obs     =     21,697
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         33
                                                              avg =      442.8
                                                              max =      1,985

Integration points =   7                        Wald chi2(18)     =    3091.58
Log likelihood = -9698.3788                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0381086   .0173556    -2.20   0.028    -.0721249   -.0040922
            ideo7 |  -.4724494    .011449   -41.27   0.000    -.4948891   -.4500097
st_ideo7_mean2021 |  -.2330551   .0962417    -2.42   0.015    -.4216855   -.0444248
     cz_imm_share |   .9905819    .227034     4.36   0.000     .5456035     1.43556
           female |   -.181629   .0368616    -4.93   0.000    -.2538763   -.1093816
              age |   .0351627   .0011648    30.19   0.000     .0328797    .0374456
  covid_diagnosed |   .4916348   .0395871    12.42   0.000     .4140455    .5692241
               hs |   .6354692   .0872224     7.29   0.000     .4645164     .806422
          college |    1.28339   .0937227    13.69   0.000     1.099697    1.467084
       income_50k |   .3620532   .0416584     8.69   0.000     .2804042    .4437023
      income_100k |   .5185306   .0559135     9.27   0.000     .4089421     .628119
             city |   .4595968   .0539835     8.51   0.000      .353791    .5654025
           suburb |   .4392961   .0497031     8.84   0.000     .3418798    .5367123
             town |   .1917242   .0606458     3.16   0.002     .0728606    .3105878
            white |   .5500946   .0750277     7.33   0.000     .4030431    .6971461
            black |   .1948536   .0883012     2.21   0.027     .0217863    .3679209
    race_hispanic |   .5453374   .0894189     6.10   0.000     .3700795    .7205953
            asian |   1.360936   .1612509     8.44   0.000      1.04489    1.676982
            _cons |   .2410065   .4092468     0.59   0.556    -.5611025    1.043116
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .1256217   .0288941      .0800351    .1971735
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 13.26       Prob >= chibar2 = 0.0001
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share female age covid_di
> agnosed hs college income_50k income_100k city suburb town white black race_hispanic asian || czone: 

Refining starting values: 

Iteration 0:   log likelihood = -9841.6137  (not concave)
Iteration 1:   log likelihood =  -9745.005  
Iteration 2:   log likelihood = -9693.1317  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -9693.1317  
Iteration 1:   log likelihood = -9689.6041  
Iteration 2:   log likelihood = -9689.4797  
Iteration 3:   log likelihood = -9689.4796  

Mixed-effects logistic regression               Number of obs     =     21,697
Group variable: czone                           Number of groups  =        622

                                                Obs per group:
                                                              min =          1
                                                              avg =       34.9
                                                              max =        938

Integration points =   7                        Wald chi2(18)     =    3076.63
Log likelihood = -9689.4796                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0437819   .0178499    -2.45   0.014    -.0787671   -.0087966
            ideo7 |   -.467298   .0115581   -40.43   0.000    -.4899514   -.4446446
cz_ideo7_mean2021 |  -.1812331   .0445844    -4.06   0.000    -.2686169   -.0938493
     cz_imm_share |   .7921111   .2411593     3.28   0.001     .3194476    1.264775
           female |  -.1822215   .0369623    -4.93   0.000    -.2546664   -.1097767
              age |   .0351655    .001166    30.16   0.000     .0328801    .0374509
  covid_diagnosed |   .4935424   .0397106    12.43   0.000     .4157111    .5713737
               hs |   .6427378   .0875126     7.34   0.000     .4712163    .8142593
          college |   1.289418    .094037    13.71   0.000     1.105108    1.473727
       income_50k |   .3557331    .041779     8.51   0.000     .2738477    .4376185
      income_100k |   .5048682   .0561421     8.99   0.000     .3948317    .6149048
             city |   .4287722   .0546561     7.84   0.000     .3216483    .5358962
           suburb |   .4039204   .0508112     7.95   0.000     .3043323    .5035084
             town |    .183937   .0609123     3.02   0.003     .0645511    .3033228
            white |   .5569138   .0752507     7.40   0.000     .4094251    .7044024
            black |   .1943378    .088261     2.20   0.028     .0213495    .3673261
    race_hispanic |   .5426677   .0895884     6.06   0.000     .3670777    .7182576
            asian |   1.368329   .1614525     8.48   0.000     1.051888     1.68477
            _cons |   .0490122   .2311891     0.21   0.832    -.4041101    .5021346
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .1664273   .0310308      .1154824    .2398465
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 17.73       Prob >= chibar2 = 0.0000
(est6 stored)

. 
. esttab using "Table1.tex", label keep(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_me
> an2021 ideo7 cz_imm_share) order(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_mean202
> 1 ideo7 cz_imm_share ) margin nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps no
> depvars                                                    
(output written to Table1.tex)

. 
. * Figure 4: The Effects of Ideological Distance on COVID-19 Vaccination Decision by Levels of Affective
>  Polarization
. 
. xtmelogit vaccinated c.st_ideo7_dist2021##c.affective_polarization_12_16_20  ideo7 cz_imm_share female 
> age covid_diagnosed hs college  city suburb town white black race_hispanic asian || inputstate: 

Refining starting values: 

Iteration 0:   log likelihood = -10802.184  (not concave)
Iteration 1:   log likelihood = -10754.115  
Iteration 2:   log likelihood = -10752.239  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -10752.239  
Iteration 1:   log likelihood = -10750.558  
Iteration 2:   log likelihood = -10750.542  
Iteration 3:   log likelihood = -10750.542  

Mixed-effects logistic regression               Number of obs     =     23,908
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         34
                                                              avg =      487.9
                                                              max =      2,187

Integration points =   7                        Wald chi2(17)     =    3379.82
Log likelihood = -10750.542                     Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------------------
                       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------------+----------------------------------------------------------------
                st_ideo7_dist2021 |   .3863327   .2598611     1.49   0.137    -.1229858    .8956512
  affective_polarization_12_16_20 |   .4860255    1.22265     0.40   0.691    -1.910324    2.882375
                                  |
              c.st_ideo7_dist2021#|
c.affective_polarization_12_16_20 |  -.9552214   .5794572    -1.65   0.099    -2.090937    .1804938
                                  |
                            ideo7 |  -.4733803   .0108881   -43.48   0.000    -.4947205     -.45204
                     cz_imm_share |   1.458271   .2091834     6.97   0.000     1.048279    1.868263
                           female |  -.2049836   .0348131    -5.89   0.000     -.273216   -.1367513
                              age |   .0346887   .0010932    31.73   0.000      .032546    .0368314
                  covid_diagnosed |   .5269721    .037414    14.08   0.000      .453642    .6003021
                               hs |   .6878082   .0833533     8.25   0.000     .5244388    .8511776
                          college |   1.441453   .0877937    16.42   0.000     1.269381    1.613526
                             city |   .4364125   .0515238     8.47   0.000     .3354277    .5373973
                           suburb |   .4758329   .0472481    10.07   0.000     .3832283    .5684375
                             town |   .1851532   .0574857     3.22   0.001     .0724833    .2978231
                            white |    .578538   .0690234     8.38   0.000     .4432547    .7138213
                            black |   .1645912   .0823781     2.00   0.046     .0031332    .3260492
                    race_hispanic |   .5383568   .0831624     6.47   0.000     .3753614    .7013521
                            asian |   1.325279   .1471762     9.00   0.000     1.036819    1.613739
                            _cons |  -.8343206   .5597382    -1.49   0.136    -1.931387    .2627462
---------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .1516079   .0287959      .1044835    .2199866
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 30.11       Prob >= chibar2 = 0.0000

. *xtmelogit vaccinated c.st_ideo7_dist2021##c.affective_polarization_12_16_20  ideo7 cz_imm_share female
>  age covid_diagnosed hs college  city suburb town white black race_hispanic asian || inputstate: 
. margins, at (st_ideo7_dist2021 = (0(1)5) affective_polarization_12_16_20 = (0.37 0.55)) predict(mu fixe
> d) 

Predictive margins                              Number of obs     =     23,908

Expression   : Predicted mean, fixed portion only, predict(mu fixed)

1._at        : st_ideo7_d~1    =           0
               affective~20    =         .37

2._at        : st_ideo7_d~1    =           0
               affective~20    =         .55

3._at        : st_ideo7_d~1    =           1
               affective~20    =         .37

4._at        : st_ideo7_d~1    =           1
               affective~20    =         .55

5._at        : st_ideo7_d~1    =           2
               affective~20    =         .37

6._at        : st_ideo7_d~1    =           2
               affective~20    =         .55

7._at        : st_ideo7_d~1    =           3
               affective~20    =         .37

8._at        : st_ideo7_d~1    =           3
               affective~20    =         .55

9._at        : st_ideo7_d~1    =           4
               affective~20    =         .37

10._at       : st_ideo7_d~1    =           4
               affective~20    =         .55

11._at       : st_ideo7_d~1    =           5
               affective~20    =         .37

12._at       : st_ideo7_d~1    =           5
               affective~20    =         .55

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7702052   .0143521    53.67   0.000     .7420757    .7983347
          2  |   .7824906   .0185114    42.27   0.000      .746209    .8187723
          3  |   .7748737   .0106971    72.44   0.000     .7539077    .7958397
          4  |   .7627743   .0150597    50.65   0.000     .7332578    .7922909
          5  |   .7794841   .0107774    72.33   0.000     .7583607    .8006075
          6  |   .7420462   .0160486    46.24   0.000     .7105915     .773501
          7  |    .784036   .0142945    54.85   0.000     .7560193    .8120528
          8  |   .7203552   .0219684    32.79   0.000      .677298    .7634125
          9  |   .7885291   .0192931    40.87   0.000     .7507153    .8263428
         10  |   .6977623   .0307844    22.67   0.000     .6374261    .7580985
         11  |   .7929629   .0247625    32.02   0.000     .7444294    .8414964
         12  |     .67434   .0411481    16.39   0.000     .5936912    .7549889
------------------------------------------------------------------------------

. marginsplot, scheme(sj) title("") ytitle("Predicted Probability") graphregion(color(white)) bgcolor(whi
> te) ///
>         legend(order(3 "Low Affective Polarization" 4 "High Affective Polarization"))

  Variables that uniquely identify margins: st_ideo7_dist2021 affective_polarization_12_16_20

. graph export "Figure4a.pdf", as(pdf) replace
(file /Users/sungkim/Dropbox/KimPelc5/Vaccination_paper/Final_PB/Replication/Figure4a.pdf written in PDF 
> format)

. 
. xtmelogit vaccinated c.cz_ideo7_dist2021##c.affective_polarization_12_16_20 ideo7 cz_imm_share female a
> ge covid_diagnosed hs college  city suburb town white black race_hispanic asian || czone:

Refining starting values: 

Iteration 0:   log likelihood = -10887.656  (not concave)
Iteration 1:   log likelihood = -10783.615  
Iteration 2:   log likelihood =  -10748.57  

Performing gradient-based optimization: 

Iteration 0:   log likelihood =  -10748.57  
Iteration 1:   log likelihood = -10744.413  
Iteration 2:   log likelihood = -10744.364  
Iteration 3:   log likelihood = -10744.364  

Mixed-effects logistic regression               Number of obs     =     23,908
Group variable: czone                           Number of groups  =        626

                                                Obs per group:
                                                              min =          1
                                                              avg =       38.2
                                                              max =      1,028

Integration points =   7                        Wald chi2(17)     =    3305.30
Log likelihood = -10744.364                     Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------------------
                       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------------+----------------------------------------------------------------
                cz_ideo7_dist2021 |   .5361338   .2640164     2.03   0.042     .0186712    1.053596
  affective_polarization_12_16_20 |   1.196092   1.089796     1.10   0.272     -.939869    3.332054
                                  |
              c.cz_ideo7_dist2021#|
c.affective_polarization_12_16_20 |  -1.287942   .5891384    -2.19   0.029    -2.442632    -.133252
                                  |
                            ideo7 |  -.4754945   .0108652   -43.76   0.000    -.4967899   -.4541991
                     cz_imm_share |   1.331119   .2450648     5.43   0.000     .8508008    1.811437
                           female |  -.2031977   .0349585    -5.81   0.000    -.2717151   -.1346802
                              age |   .0346819   .0010973    31.61   0.000     .0325313    .0368325
                  covid_diagnosed |   .5292095   .0376021    14.07   0.000     .4555108    .6029083
                               hs |   .6904872   .0837579     8.24   0.000     .5263247    .8546496
                          college |    1.44179   .0882215    16.34   0.000     1.268879    1.614701
                             city |   .4254779   .0522269     8.15   0.000     .3231152    .5278407
                           suburb |   .4601505   .0484406     9.50   0.000     .3652086    .5550924
                             town |   .1829681   .0578231     3.16   0.002     .0696368    .2962993
                            white |   .5836825    .069331     8.42   0.000     .4477963    .7195687
                            black |   .1703656   .0827284     2.06   0.039      .008221    .3325103
                    race_hispanic |   .5346897   .0835351     6.40   0.000      .370964    .6984155
                            asian |   1.321946   .1477284     8.95   0.000     1.032404    1.611488
                            _cons |  -1.161638    .502396    -2.31   0.021    -2.146316   -.1769595
---------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .2203727   .0301032      .1686096    .2880269
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 41.19       Prob >= chibar2 = 0.0000

. margins, at (cz_ideo7_dist2021 = (0(1)5) affective_polarization_12_16_20 = (0.37 0.55)) predict(mu fixe
> d)

Predictive margins                              Number of obs     =     23,908

Expression   : Predicted mean, fixed portion only, predict(mu fixed)

1._at        : cz_ideo7_d~1    =           0
               affective~20    =         .37

2._at        : cz_ideo7_d~1    =           0
               affective~20    =         .55

3._at        : cz_ideo7_d~1    =           1
               affective~20    =         .37

4._at        : cz_ideo7_d~1    =           1
               affective~20    =         .55

5._at        : cz_ideo7_d~1    =           2
               affective~20    =         .37

6._at        : cz_ideo7_d~1    =           2
               affective~20    =         .55

7._at        : cz_ideo7_d~1    =           3
               affective~20    =         .37

8._at        : cz_ideo7_d~1    =           3
               affective~20    =         .55

9._at        : cz_ideo7_d~1    =           4
               affective~20    =         .37

10._at       : cz_ideo7_d~1    =           4
               affective~20    =         .55

11._at       : cz_ideo7_d~1    =           5
               affective~20    =         .37

12._at       : cz_ideo7_d~1    =           5
               affective~20    =         .55

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .756689   .0133825    56.54   0.000     .7304597    .7829183
          2  |   .7872844   .0160873    48.94   0.000     .7557539     .818815
          3  |    .765405   .0092173    83.04   0.000     .7473394    .7834706
          4  |   .7630056   .0120016    63.58   0.000      .739483    .7865283
          5  |   .7739335   .0096322    80.35   0.000     .7550548    .7928123
          6  |   .7371704   .0134434    54.84   0.000     .7108219    .7635188
          7  |   .7822715   .0137749    56.79   0.000     .7552731    .8092698
          8  |   .7098744   .0207426    34.22   0.000     .6692195    .7505292
          9  |   .7904162   .0190602    41.47   0.000     .7530589    .8277735
         10  |   .6812418   .0308183    22.11   0.000     .6208391    .7416445
         11  |   .7983656   .0245191    32.56   0.000      .750309    .8464222
         12  |   .6514232    .042253    15.42   0.000     .5686088    .7342377
------------------------------------------------------------------------------

. marginsplot, scheme(sj) title("") ytitle("Predicted Probability") graphregion(color(white)) bgcolor(whi
> te) ///
>         legend(order(3 "Low Affective Polarization" 4 "High Affective Polarization"))

  Variables that uniquely identify margins: cz_ideo7_dist2021 affective_polarization_12_16_20

. graph export "Figure4b.pdf", as(pdf) replace
(file /Users/sungkim/Dropbox/KimPelc5/Vaccination_paper/Final_PB/Replication/Figure4b.pdf written in PDF 
> format)

. 
. 
. * Table A1: The Effects of Ideological Distance on COVID-19 Vaccination Decision
. 
. esttab using "TableA1.tex", label keep(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_m
> ean2021 ideo7 cz_imm_share female age covid_diagnosed hs college income_50k income_100k city suburb tow
> n white black race_hispanic asian covid_diagnosed) order(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_d
> ist2021 cz_ideo7_mean2021 ideo7 cz_imm_share female age covid_diagnosed hs college income_50k income_10
> 0k city suburb town white black race_hispanic asian covid_diagnosed) margin nonotes se(3) b(3) replace 
> star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)
(output written to TableA1.tex)

. 
. * Table A2: Results: Excluding Income Variables
. 
. eststo clear

. 
. eststo: regress vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share female age covid_diag
> nosed hs college city suburb town white black race_hispanic asian , cluster(inputstate)

Linear regression                               Number of obs     =     23,908
                                                F(16, 48)         =     172.79
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1831
                                                Root MSE          =     .38531

                                 (Std. Err. adjusted for 49 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0242712   .0023118   -10.50   0.000    -.0289194    -.019623
            ideo7 |  -.0695759   .0019923   -34.92   0.000    -.0735817   -.0655702
st_ideo7_mean2021 |  -.0481787   .0127616    -3.78   0.000    -.0738377   -.0225197
     cz_imm_share |   .1115268    .035172     3.17   0.003     .0408088    .1822448
           female |  -.0337478   .0053206    -6.34   0.000    -.0444456   -.0230501
              age |   .0050497   .0002384    21.18   0.000     .0045704     .005529
  covid_diagnosed |   .0831092   .0075305    11.04   0.000     .0679681    .0982502
               hs |   .1387663   .0184664     7.51   0.000     .1016371    .1758955
          college |   .2391228   .0173661    13.77   0.000     .2042059    .2740398
             city |   .0735054    .008679     8.47   0.000      .056055    .0909557
           suburb |   .0832181    .007944    10.48   0.000     .0672456    .0991907
             town |   .0374731   .0101511     3.69   0.001      .017063    .0578832
            white |   .0925481   .0149255     6.20   0.000     .0625383    .1225579
            black |   .0408271   .0151812     2.69   0.010     .0103033     .071351
    race_hispanic |   .0881233   .0157235     5.60   0.000      .056509    .1197375
            asian |   .1775087   .0211922     8.38   0.000     .1348989    .2201184
            _cons |   .6294766   .0497742    12.65   0.000     .5293989    .7295543
-----------------------------------------------------------------------------------
(est1 stored)

. eststo: areg  vaccinated st_ideo7_dist2021 ideo7 cz_imm_share female age covid_diagnosed hs college cit
> y suburb town white black race_hispanic asian , cluster(inputstate) absorb(inputstate)

Linear regression, absorbing indicators         Number of obs     =     23,908
Absorbed variable: inputstate                   No. of categories =         49
                                                F(  15,     48)   =     159.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1866
                                                Adj R-squared     =     0.1845
                                                Root MSE          =     0.3849

                                 (Std. Err. adjusted for 49 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0239799   .0022819   -10.51   0.000    -.0285679   -.0193918
            ideo7 |  -.0695572   .0019997   -34.78   0.000    -.0735778   -.0655365
     cz_imm_share |   .1942191   .0416471     4.66   0.000     .1104821    .2779562
           female |  -.0335711   .0053674    -6.25   0.000    -.0443631   -.0227791
              age |   .0051148   .0002391    21.39   0.000     .0046341    .0055955
  covid_diagnosed |   .0831454   .0075259    11.05   0.000     .0680136    .0982773
               hs |   .1382247   .0183359     7.54   0.000     .1013579    .1750915
          college |   .2360179   .0174074    13.56   0.000      .201018    .2710177
             city |   .0750541   .0090711     8.27   0.000     .0568153    .0932929
           suburb |   .0826763   .0076146    10.86   0.000     .0673662    .0979865
             town |    .036459    .009989     3.65   0.001     .0163747    .0565433
            white |   .0900291   .0150147     6.00   0.000     .0598401    .1202181
            black |    .033167   .0153329     2.16   0.036     .0023381    .0639958
    race_hispanic |   .0905365   .0164212     5.51   0.000     .0575194    .1235535
            asian |   .1775777   .0206805     8.59   0.000     .1359968    .2191585
            _cons |   .4282139   .0353563    12.11   0.000     .3571252    .4993025
-----------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: regress vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share female age covid_diag
> nosed hs college city suburb town white black race_hispanic asian , cluster(czone)

Linear regression                               Number of obs     =     23,908
                                                F(16, 625)        =     139.90
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1832
                                                Root MSE          =     .38528

                                     (Std. Err. adjusted for 626 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0223887   .0026055    -8.59   0.000    -.0275053    -.017272
            ideo7 |  -.0682281   .0022134   -30.82   0.000    -.0725748   -.0638814
cz_ideo7_mean2021 |  -.0416506   .0069573    -5.99   0.000    -.0553131    -.027988
     cz_imm_share |   .0998262   .0346155     2.88   0.004     .0318494    .1678029
           female |  -.0335194    .005208    -6.44   0.000    -.0437466   -.0232922
              age |    .005031   .0002167    23.21   0.000     .0046054    .0054567
  covid_diagnosed |   .0824944   .0065319    12.63   0.000     .0696673    .0953215
               hs |   .1390685   .0179314     7.76   0.000     .1038554    .1742817
          college |   .2371347   .0175112    13.54   0.000     .2027467    .2715226
             city |   .0685683   .0093824     7.31   0.000     .0501435    .0869932
           suburb |    .077756   .0080779     9.63   0.000      .061893     .093619
             town |   .0361045   .0101837     3.55   0.000     .0161061    .0561028
            white |   .0924053   .0136746     6.76   0.000     .0655517     .119259
            black |   .0389768   .0148612     2.62   0.009     .0097928    .0681607
    race_hispanic |   .0894914   .0154426     5.80   0.000     .0591658     .119817
            asian |   .1791147   .0190327     9.41   0.000     .1417391    .2164904
            _cons |   .6026315   .0392052    15.37   0.000     .5256416    .6796214
-----------------------------------------------------------------------------------
(est3 stored)

. eststo: areg vaccinated cz_ideo7_dist2021 ideo7 female age covid_diagnosed hs college city suburb town 
> white black race_hispanic asian, cluster(czone) absorb(czone)

Linear regression, absorbing indicators         Number of obs     =     23,908
Absorbed variable: czone                        No. of categories =        626
                                                F(  14,    625)   =     106.45
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2147
                                                Adj R-squared     =     0.1932
                                                Root MSE          =     0.3828

                                     (Std. Err. adjusted for 626 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0219169   .0026766    -8.19   0.000    -.0271731   -.0166607
            ideo7 |  -.0684346   .0022488   -30.43   0.000    -.0728507   -.0640185
           female |  -.0319911     .00529    -6.05   0.000    -.0423794   -.0216028
              age |   .0050052   .0002221    22.53   0.000      .004569    .0054415
  covid_diagnosed |   .0829756   .0067629    12.27   0.000     .0696948    .0962564
               hs |   .1369682   .0184481     7.42   0.000     .1007405    .1731959
          college |   .2307383   .0179949    12.82   0.000     .1954005    .2660762
             city |   .0646057   .0104161     6.20   0.000     .0441509    .0850604
           suburb |   .0706144   .0093085     7.59   0.000     .0523347    .0888941
             town |   .0350534   .0107311     3.27   0.001     .0139801    .0561267
            white |   .0883803   .0141994     6.22   0.000      .060496    .1162647
            black |    .029398   .0147571     1.99   0.047     .0004184    .0583776
    race_hispanic |   .0903444   .0159188     5.68   0.000     .0590836    .1216052
            asian |   .1745156   .0189066     9.23   0.000     .1373874    .2116437
            _cons |   .4674904   .0275953    16.94   0.000     .4132995    .5216812
-----------------------------------------------------------------------------------
(est4 stored)

. 
. eststo: xtmelogit vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share female age covid_di
> agnosed hs college city suburb town white black race_hispanic asian || inputstate: 

Refining starting values: 

Iteration 0:   log likelihood = -10806.185  (not concave)
Iteration 1:   log likelihood =  -10754.39  
Iteration 2:   log likelihood = -10750.863  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -10750.863  
Iteration 1:   log likelihood = -10749.714  
Iteration 2:   log likelihood = -10749.707  
Iteration 3:   log likelihood = -10749.707  

Mixed-effects logistic regression               Number of obs     =     23,908
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         34
                                                              avg =      487.9
                                                              max =      2,187

Integration points =   7                        Wald chi2(16)     =    3378.18
Log likelihood = -10749.707                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0429914   .0166097    -2.59   0.010    -.0755459    -.010437
            ideo7 |  -.4707962   .0108921   -43.22   0.000    -.4921443   -.4494481
st_ideo7_mean2021 |  -.2308933   .0966986    -2.39   0.017    -.4204192   -.0413674
     cz_imm_share |   1.297232   .2203035     5.89   0.000      .865445    1.729019
           female |  -.2046605   .0348127    -5.88   0.000     -.272892   -.1364289
              age |   .0345932   .0010935    31.64   0.000       .03245    .0367363
  covid_diagnosed |   .5293236   .0374167    14.15   0.000     .4559882     .602659
               hs |   .6885718   .0833351     8.26   0.000      .525238    .8519057
          college |   1.442852   .0877745    16.44   0.000     1.270817    1.614887
             city |   .4367307   .0514961     8.48   0.000     .3358003    .5376611
           suburb |   .4748504   .0472311    10.05   0.000     .3822792    .5674216
             town |   .1838462   .0574789     3.20   0.001     .0711896    .2965027
            white |   .5807618   .0689972     8.42   0.000     .4455298    .7159938
            black |   .1706199   .0823173     2.07   0.038     .0092809    .3319589
    race_hispanic |   .5395296   .0831151     6.49   0.000      .376627    .7024323
            asian |   1.316163   .1470885     8.95   0.000     1.027875    1.604451
            _cons |   .3091659   .4081946     0.76   0.449    -.4908809    1.109213
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |    .140379   .0285422      .0942396     .209108
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 22.23       Prob >= chibar2 = 0.0000
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share female age covid_di
> agnosed hs college city suburb town white black race_hispanic asian || czone: 

Refining starting values: 

Iteration 0:   log likelihood = -10890.058  (not concave)
Iteration 1:   log likelihood = -10784.663  
Iteration 2:   log likelihood = -10758.815  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -10758.815  
Iteration 1:   log likelihood =  -10748.34  
Iteration 2:   log likelihood =  -10744.37  
Iteration 3:   log likelihood = -10739.903  
Iteration 4:   log likelihood =  -10738.95  
Iteration 5:   log likelihood = -10738.949  

Mixed-effects logistic regression               Number of obs     =     23,908
Group variable: czone                           Number of groups  =        626

                                                Obs per group:
                                                              min =          1
                                                              avg =       38.2
                                                              max =      1,028

Integration points =   7                        Wald chi2(16)     =    3340.09
Log likelihood = -10738.949                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0485325    .017075    -2.84   0.004    -.0819989   -.0150662
            ideo7 |  -.4662036   .0109993   -42.38   0.000    -.4877619   -.4446454
cz_ideo7_mean2021 |  -.1808014   .0439189    -4.12   0.000    -.2668809   -.0947219
     cz_imm_share |   .9713197   .2436225     3.99   0.000     .4938284    1.448811
           female |  -.2041406   .0349288    -5.84   0.000    -.2725999   -.1356814
              age |   .0345737   .0010961    31.54   0.000     .0324254     .036722
  covid_diagnosed |   .5316101   .0375554    14.16   0.000     .4580029    .6052173
               hs |   .6932427   .0836808     8.28   0.000     .5292314     .857254
          college |   1.443019   .0881321    16.37   0.000     1.270283    1.615755
             city |   .4070119   .0522721     7.79   0.000     .3045605    .5094633
           suburb |   .4391639   .0484946     9.06   0.000     .3441163    .5342115
             town |   .1753191   .0577943     3.03   0.002     .0620443    .2885939
            white |    .586262    .069242     8.47   0.000     .4505501    .7219738
            black |   .1708504   .0824041     2.07   0.038     .0093413    .3323596
    race_hispanic |   .5380888    .083375     6.45   0.000     .3746767    .7015009
            asian |   1.314694   .1473814     8.92   0.000     1.025832    1.603556
            _cons |   .1341767   .2253764     0.60   0.552    -.3075529    .5759063
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .1892426   .0305081      .1379735    .2595627
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 27.44       Prob >= chibar2 = 0.0000
(est6 stored)

. 
. esttab using "TableA2.tex", label keep(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_m
> ean2021 ideo7 cz_imm_share female age covid_diagnosed hs college city suburb town white black race_hisp
> anic asian covid_diagnosed) order(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_mean20
> 21 ideo7 cz_imm_share female age covid_diagnosed hs college city suburb town white black race_hispanic 
> asian covid_diagnosed) margin nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps no
> depvars 
(output written to TableA2.tex)

. 
. * Table A3: Results: Subset of Data with Individuals Identifying as Liberals
. 
. eststo clear

. 
. eststo: regress vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share female age covid_diag
> nosed hs college city suburb town white black race_hispanic asian if ideo5 == 1 | ideo5 == 2, cluster(i
> nputstate)

Linear regression                               Number of obs     =      8,150
                                                F(16, 48)         =      47.48
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0971
                                                Root MSE          =     .26601

                                 (Std. Err. adjusted for 49 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0257523   .0135295    -1.90   0.063    -.0529552    .0014506
            ideo7 |  -.0439146   .0130818    -3.36   0.002    -.0702174   -.0176118
st_ideo7_mean2021 |  -.0095221   .0178934    -0.53   0.597    -.0454992     .026455
     cz_imm_share |   .0322801   .0406115     0.79   0.431    -.0493748     .113935
           female |  -.0151802   .0049919    -3.04   0.004     -.025217   -.0051434
              age |   .0023633   .0002095    11.28   0.000     .0019422    .0027845
  covid_diagnosed |   .0610086   .0101378     6.02   0.000     .0406251    .0813921
               hs |   .1731173   .0313335     5.52   0.000     .1101171    .2361175
          college |   .2416502   .0332279     7.27   0.000     .1748409    .3084594
             city |   .0386823   .0110816     3.49   0.001     .0164013    .0609632
           suburb |   .0629557   .0085209     7.39   0.000     .0458233     .080088
             town |    .022294   .0112487     1.98   0.053     -.000323     .044911
            white |   .0457985   .0135968     3.37   0.001     .0184604    .0731367
            black |  -.0438731   .0157522    -2.79   0.008    -.0755451   -.0122011
    race_hispanic |   .0104172   .0156157     0.67   0.508    -.0209802    .0418146
            asian |   .0929511   .0175709     5.29   0.000     .0576225    .1282796
            _cons |   .6573283   .0617571    10.64   0.000     .5331574    .7814992
-----------------------------------------------------------------------------------
(est1 stored)

. eststo: areg  vaccinated st_ideo7_dist2021 ideo7 cz_imm_share female age covid_diagnosed hs college cit
> y suburb town white black race_hispanic asian if ideo5 == 1 | ideo5 == 2, cluster(inputstate) absorb(in
> putstate)

Linear regression, absorbing indicators         Number of obs     =      8,150
Absorbed variable: inputstate                   No. of categories =         49
                                                F(  15,     48)   =      47.48
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1020
                                                Adj R-squared     =     0.0950
                                                Root MSE          =     0.2661

                                 (Std. Err. adjusted for 49 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0256056   .0135881    -1.88   0.066    -.0529263    .0017151
            ideo7 |  -.0433762   .0130414    -3.33   0.002    -.0695976   -.0171548
     cz_imm_share |   .0592123   .0578472     1.02   0.311    -.0570973    .1755219
           female |  -.0155214   .0050176    -3.09   0.003    -.0256099   -.0054329
              age |   .0023818   .0002109    11.29   0.000     .0019578    .0028058
  covid_diagnosed |   .0612986   .0101943     6.01   0.000     .0408016    .0817957
               hs |   .1738329   .0316069     5.50   0.000     .1102831    .2373828
          college |   .2413663   .0337423     7.15   0.000     .1735227    .3092098
             city |    .037839   .0118561     3.19   0.002     .0140007    .0616774
           suburb |    .062348   .0091218     6.84   0.000     .0440073    .0806887
             town |   .0201704   .0116264     1.73   0.089    -.0032061     .043547
            white |   .0443955    .013763     3.23   0.002     .0167232    .0720677
            black |   -.041836   .0164673    -2.54   0.014    -.0749456   -.0087263
    race_hispanic |   .0105603   .0155644     0.68   0.501    -.0207339    .0418546
            asian |   .0941628   .0174034     5.41   0.000      .059171    .1291546
            _cons |   .6148952   .0564182    10.90   0.000     .5014589    .7283315
-----------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: regress vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share female age covid_diag
> nosed hs college city suburb town white black race_hispanic asian if ideo5 == 1 | ideo5 == 2, cluster(c
> zone)

Linear regression                               Number of obs     =      8,150
                                                F(16, 482)        =      29.90
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0970
                                                Root MSE          =     .26603

                                     (Std. Err. adjusted for 483 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0294734   .0157993    -1.87   0.063    -.0605174    .0015707
            ideo7 |  -.0462503   .0147187    -3.14   0.002    -.0751709   -.0173296
cz_ideo7_mean2021 |    .014366   .0180312     0.80   0.426    -.0210635    .0497955
     cz_imm_share |   .0503589   .0318306     1.58   0.114    -.0121851    .1129028
           female |  -.0152293   .0055816    -2.73   0.007    -.0261965   -.0042621
              age |   .0023567   .0001917    12.30   0.000     .0019801    .0027333
  covid_diagnosed |   .0600435   .0087137     6.89   0.000     .0429219     .077165
               hs |    .173618   .0352506     4.93   0.000     .1043542    .2428818
          college |   .2418309   .0353196     6.85   0.000     .1724316    .3112303
             city |   .0374209    .012846     2.91   0.004     .0121798    .0626619
           suburb |   .0612202   .0111344     5.50   0.000     .0393422    .0830981
             town |   .0222548   .0143309     1.55   0.121    -.0059039    .0504135
            white |   .0462682    .016664     2.78   0.006     .0135252    .0790113
            black |   -.045365   .0204429    -2.22   0.027    -.0855333   -.0051968
    race_hispanic |   .0104598   .0190693     0.55   0.584    -.0270094    .0479289
            asian |   .0943553   .0186107     5.07   0.000     .0577871    .1309235
            _cons |   .5745035   .0519404    11.06   0.000     .4724459    .6765611
-----------------------------------------------------------------------------------
(est3 stored)

. eststo: areg vaccinated cz_ideo7_dist2021 ideo7 female age covid_diagnosed hs college city suburb town 
> white black race_hispanic asian if ideo5 == 1 | ideo5 == 2, cluster(czone) absorb(czone)

Linear regression, absorbing indicators         Number of obs     =      8,150
Absorbed variable: czone                        No. of categories =        483
                                                F(  14,    482)   =      29.09
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1743
                                                Adj R-squared     =     0.1208
                                                Root MSE          =     0.2622

                                     (Std. Err. adjusted for 483 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0303371   .0163489    -1.86   0.064     -.062461    .0017868
            ideo7 |  -.0467307   .0152439    -3.07   0.002    -.0766834   -.0167781
           female |   -.014073   .0056747    -2.48   0.013    -.0252232   -.0029228
              age |   .0023608   .0002025    11.66   0.000     .0019629    .0027587
  covid_diagnosed |   .0596534   .0090843     6.57   0.000     .0418037    .0775031
               hs |    .171735    .037293     4.61   0.000     .0984581     .245012
          college |   .2373825   .0376666     6.30   0.000     .1633714    .3113935
             city |   .0308468   .0148631     2.08   0.038     .0016424    .0600512
           suburb |   .0551347   .0126487     4.36   0.000     .0302814     .079988
             town |     .01169   .0164825     0.71   0.479    -.0206965    .0440765
            white |   .0415151   .0176287     2.35   0.019     .0068766    .0761536
            black |  -.0429551   .0217672    -1.97   0.049    -.0857255   -.0001848
    race_hispanic |   .0068574    .020146     0.34   0.734    -.0327273    .0464422
            asian |   .0938652   .0195722     4.80   0.000     .0554079    .1323226
            _cons |    .651595   .0759897     8.57   0.000      .502283    .8009071
-----------------------------------------------------------------------------------
(est4 stored)

.  
. eststo: xtmelogit vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share female age covid_di
> agnosed hs college city suburb town white black race_hispanic asian || inputstate: if ideo5 == 1 | ideo
> 5 == 2

Refining starting values: 

Iteration 0:   log likelihood = -2042.3906  (not concave)
Iteration 1:   log likelihood = -2012.5462  
Iteration 2:   log likelihood = -2011.0885  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -2011.0885  
Iteration 1:   log likelihood = -2010.2411  
Iteration 2:   log likelihood = -2007.6268  
Iteration 3:   log likelihood = -2006.6225  
Iteration 4:   log likelihood = -2006.5916  
Iteration 5:   log likelihood = -2006.5916  

Mixed-effects logistic regression               Number of obs     =      8,150
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =          4
                                                              avg =      166.3
                                                              max =        895

Integration points =   7                        Wald chi2(16)     =     620.83
Log likelihood = -2006.5916                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0744267   .0864479    -0.86   0.389    -.2438615    .0950081
            ideo7 |  -.3141058   .0709281    -4.43   0.000    -.4531223   -.1750893
st_ideo7_mean2021 |  -.3720506   .1992814    -1.87   0.062    -.7626349    .0185338
     cz_imm_share |   .6576891   .4340159     1.52   0.130    -.1929664    1.508345
           female |  -.2116135   .0882753    -2.40   0.017    -.3846298   -.0385972
              age |   .0329029   .0026472    12.43   0.000     .0277145    .0380913
  covid_diagnosed |   .7226454   .0917406     7.88   0.000     .5428372    .9024536
               hs |   1.053678   .1822995     5.78   0.000      .696378    1.410979
          college |   2.166147   .1955734    11.08   0.000      1.78283    2.549464
             city |   .3703369   .1259036     2.94   0.003     .1235703    .6171035
           suburb |   .7802468   .1307503     5.97   0.000      .523981    1.036513
             town |   .2058064   .1521601     1.35   0.176    -.0924219    .5040346
            white |    .606046   .1645468     3.68   0.000     .2835401    .9285518
            black |   -.366784   .1768909    -2.07   0.038    -.7134838   -.0200842
    race_hispanic |   .1046926    .185504     0.56   0.573    -.2588885    .4682736
            asian |   1.606353   .3970337     4.05   0.000     .8281815    2.384525
            _cons |   .4807397   .7939745     0.61   0.545    -1.075422    2.036901
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   4.24e-08   .0729403             0           .
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 0.00        Prob >= chibar2 = 1.0000
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share female age covid_di
> agnosed hs college city suburb town white black race_hispanic asian || czone: if ideo5 == 1 | ideo5 == 
> 2

Refining starting values: 

Iteration 0:   log likelihood = -2041.3394  
Iteration 1:   log likelihood = -2010.2799  
Iteration 2:   log likelihood = -2006.8023  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -2006.8023  
Iteration 1:   log likelihood = -2006.6384  
Iteration 2:   log likelihood = -2006.6373  
Iteration 3:   log likelihood = -2006.6373  

Mixed-effects logistic regression               Number of obs     =      8,150
Group variable: czone                           Number of groups  =        483

                                                Obs per group:
                                                              min =          1
                                                              avg =       16.9
                                                              max =        411

Integration points =   7                        Wald chi2(16)     =     605.10
Log likelihood = -2006.6373                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.1287202   .0877622    -1.47   0.142    -.3007309    .0432905
            ideo7 |  -.3493299   .0707972    -4.93   0.000    -.4880899     -.21057
cz_ideo7_mean2021 |  -.0006778   .1345894    -0.01   0.996    -.2644681    .2631126
     cz_imm_share |    1.21645   .4984732     2.44   0.015     .2394604    2.193439
           female |  -.2126473   .0887569    -2.40   0.017    -.3866077    -.038687
              age |   .0330604   .0026704    12.38   0.000     .0278265    .0382942
  covid_diagnosed |   .7162396   .0922816     7.76   0.000     .5353709    .8971082
               hs |   1.059773   .1837157     5.77   0.000     .6996968    1.419849
          college |   2.173819   .1973787    11.01   0.000     1.786964    2.560674
             city |   .3546177   .1277128     2.78   0.005     .1043052    .6049301
           suburb |   .7620977   .1327732     5.74   0.000      .501867    1.022328
             town |   .2094628   .1531707     1.37   0.171    -.0907462    .5096719
            white |   .6060523   .1656517     3.66   0.000     .2813809    .9307236
            black |  -.3995469   .1784364    -2.24   0.025    -.7492759    -.049818
    race_hispanic |   .0954285   .1870674     0.51   0.610    -.2712169    .4620739
            asian |   1.622587   .3978759     4.08   0.000     .8427644    2.402409
            _cons |  -.8415878   .5381257    -1.56   0.118    -1.896295    .2131192
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .2342171   .0882545       .111913    .4901814
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 3.03        Prob >= chibar2 = 0.0410
(est6 stored)

. 
. esttab using "TableA3.tex", label keep(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_m
> ean2021 ideo7 cz_imm_share female age covid_diagnosed hs college city suburb town white black race_hisp
> anic asian covid_diagnosed) order(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_mean20
> 21 ideo7 cz_imm_share female age covid_diagnosed hs college city suburb town white black race_hispanic 
> asian covid_diagnosed) margin nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps no
> depvars 
(output written to TableA3.tex)

. 
. * Table A4: Results: Subset of Data with Individuals Identifying as Conservatives
. 
. eststo clear

. 
. eststo: regress vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share female age covid_diag
> nosed hs college city suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(i
> nputstate)

Linear regression                               Number of obs     =      7,315
                                                F(16, 48)         =     114.72
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1137
                                                Root MSE          =     .46307

                                 (Std. Err. adjusted for 49 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |   -.032574   .0113238    -2.88   0.006    -.0553421   -.0098059
            ideo7 |  -.0509767   .0073845    -6.90   0.000    -.0658242   -.0361292
st_ideo7_mean2021 |  -.0774745    .031267    -2.48   0.017    -.1403411    -.014608
     cz_imm_share |    .169701   .0869818     1.95   0.057    -.0051876    .3445896
           female |  -.0566212   .0111708    -5.07   0.000    -.0790815   -.0341609
              age |   .0071342   .0003784    18.85   0.000     .0063734    .0078951
  covid_diagnosed |   .0943139   .0138772     6.80   0.000     .0664118     .122216
               hs |   .0634743   .0351976     1.80   0.078    -.0072952    .1342439
          college |   .1722854   .0352087     4.89   0.000     .1014936    .2430772
             city |   .0853698   .0166302     5.13   0.000     .0519326    .1188069
           suburb |   .0808267   .0153944     5.25   0.000     .0498743    .1117792
             town |   .0358331   .0189052     1.90   0.064    -.0021783    .0738444
            white |   .1641061   .0287753     5.70   0.000     .1062495    .2219627
            black |   .1613762   .0380841     4.24   0.000     .0848029    .2379495
    race_hispanic |   .1764337   .0422601     4.17   0.000     .0914641    .2614034
            asian |   .2763983   .0365514     7.56   0.000     .2029068    .3498897
            _cons |   .5130005   .1120806     4.58   0.000     .2876473    .7383536
-----------------------------------------------------------------------------------
(est1 stored)

. eststo: areg  vaccinated st_ideo7_dist2021 ideo7 cz_imm_share female age covid_diagnosed hs college cit
> y suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(inputstate) absorb(in
> putstate)

Linear regression, absorbing indicators         Number of obs     =      7,315
Absorbed variable: inputstate                   No. of categories =         49
                                                F(  15,     48)   =      95.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1277
                                                Adj R-squared     =     0.1201
                                                Root MSE          =     0.4609

                                 (Std. Err. adjusted for 49 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0299087   .0112063    -2.67   0.010    -.0524404   -.0073771
            ideo7 |  -.0520831   .0075297    -6.92   0.000    -.0672225   -.0369437
     cz_imm_share |   .3303078   .0616796     5.36   0.000     .2062928    .4543229
           female |  -.0557305   .0112567    -4.95   0.000    -.0783635   -.0330975
              age |    .007297   .0003926    18.59   0.000     .0065077    .0080863
  covid_diagnosed |   .0963031   .0133959     7.19   0.000     .0693689    .1232373
               hs |   .0674257   .0348884     1.93   0.059    -.0027221    .1375735
          college |   .1711366   .0356562     4.80   0.000      .099445    .2428281
             city |   .0887322   .0178547     4.97   0.000     .0528329    .1246315
           suburb |   .0799365   .0151571     5.27   0.000     .0494611     .110412
             town |   .0310636   .0185396     1.68   0.100    -.0062128    .0683401
            white |   .1562596   .0291774     5.36   0.000     .0975944    .2149247
            black |   .1425976   .0394738     3.61   0.001     .0632303    .2219649
    race_hispanic |   .1819726    .043156     4.22   0.000     .0952016    .2687435
            asian |   .2781466   .0328824     8.46   0.000     .2120321     .344261
            _cons |   .1789096   .0725907     2.46   0.017     .0329563    .3248629
-----------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: regress vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share female age covid_diag
> nosed hs college city suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(c
> zone)

Linear regression                               Number of obs     =      7,315
                                                F(16, 556)        =      50.25
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1137
                                                Root MSE          =     .46305

                                     (Std. Err. adjusted for 557 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0321252   .0109106    -2.94   0.003    -.0535562   -.0106942
            ideo7 |  -.0506324   .0081046    -6.25   0.000    -.0665518    -.034713
cz_ideo7_mean2021 |  -.0577522    .017037    -3.39   0.001    -.0912169   -.0242875
     cz_imm_share |   .1652162   .0863119     1.91   0.056     -.004321    .3347534
           female |  -.0569563   .0108756    -5.24   0.000    -.0783186   -.0355941
              age |   .0071425   .0003947    18.09   0.000     .0063671    .0079178
  covid_diagnosed |   .0945296   .0125107     7.56   0.000     .0699557    .1191035
               hs |   .0658795   .0305178     2.16   0.031     .0059353    .1258237
          college |   .1741846   .0321256     5.42   0.000     .1110821     .237287
             city |   .0805566   .0188199     4.28   0.000     .0435898    .1175234
           suburb |    .074534   .0148997     5.00   0.000     .0452673    .1038006
             town |   .0353285   .0193441     1.83   0.068    -.0026679    .0733249
            white |   .1630886   .0245022     6.66   0.000     .1149604    .2112168
            black |   .1574363   .0338802     4.65   0.000     .0908876    .2239851
    race_hispanic |    .175488   .0320245     5.48   0.000     .1125841    .2383918
            asian |   .2793628   .0471784     5.92   0.000     .1866931    .3720324
            _cons |   .4375146   .0783928     5.58   0.000     .2835324    .5914968
-----------------------------------------------------------------------------------
(est3 stored)

. eststo: areg vaccinated cz_ideo7_dist2021 ideo7 female age covid_diagnosed hs college city suburb town 
> white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(czone) absorb(czone)

Linear regression, absorbing indicators         Number of obs     =      7,315
Absorbed variable: czone                        No. of categories =        557
                                                F(  14,    556)   =      45.08
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1964
                                                Adj R-squared     =     0.1285
                                                Root MSE          =     0.4587

                                     (Std. Err. adjusted for 557 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |   -.025675   .0111429    -2.30   0.022    -.0475623   -.0037877
            ideo7 |  -.0521732   .0081952    -6.37   0.000    -.0682706   -.0360758
           female |  -.0492709    .011899    -4.14   0.000    -.0726433   -.0258984
              age |   .0072742   .0004301    16.91   0.000     .0064293    .0081191
  covid_diagnosed |   .0965521   .0136566     7.07   0.000     .0697272    .1233769
               hs |   .0670122   .0335322     2.00   0.046     .0011469    .1328776
          college |   .1771335   .0355304     4.99   0.000     .1073433    .2469237
             city |   .0859984   .0218543     3.94   0.000     .0430713    .1289255
           suburb |   .0728039   .0184424     3.95   0.000     .0365786    .1090292
             town |   .0267559   .0216822     1.23   0.218    -.0158332    .0693449
            white |   .1602474   .0267115     6.00   0.000     .1077796    .2127152
            black |   .1357828   .0360782     3.76   0.000     .0649166    .2066491
    race_hispanic |   .1838527   .0339442     5.42   0.000     .1171781    .2505273
            asian |    .274344   .0460681     5.96   0.000     .1838552    .3648328
            _cons |   .2108333   .0613991     3.43   0.001     .0902308    .3314358
-----------------------------------------------------------------------------------
(est4 stored)

. 
. eststo: xtmelogit vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share female age covid_di
> agnosed hs college city suburb town white black race_hispanic asian || inputstate: if ideo5 == 4 | ideo
> 5 == 5

Refining starting values: 

Iteration 0:   log likelihood = -4528.4962  (not concave)
Iteration 1:   log likelihood = -4504.0287  
Iteration 2:   log likelihood = -4494.2227  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -4494.2227  
Iteration 1:   log likelihood = -4491.9589  
Iteration 2:   log likelihood = -4491.9288  
Iteration 3:   log likelihood = -4491.9288  

Mixed-effects logistic regression               Number of obs     =      7,315
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         11
                                                              avg =      149.3
                                                              max =        609

Integration points =   7                        Wald chi2(16)     =     727.50
Log likelihood = -4491.9288                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.1464215   .0495121    -2.96   0.003    -.2434635   -.0493795
            ideo7 |  -.2427914   .0394706    -6.15   0.000    -.3201523   -.1654305
st_ideo7_mean2021 |  -.3389842   .1477411    -2.29   0.022    -.6285514    -.049417
     cz_imm_share |   1.372515   .3379174     4.06   0.000     .7102091    2.034821
           female |  -.2617178   .0514615    -5.09   0.000    -.3625806   -.1608551
              age |   .0334906   .0016853    19.87   0.000     .0301874    .0367938
  covid_diagnosed |   .4428585   .0571167     7.75   0.000     .3309119    .5548051
               hs |   .2935249   .1422104     2.06   0.039     .0147976    .5722521
          college |   .8016212   .1482115     5.41   0.000     .5111321     1.09211
             city |   .3944968   .0796792     4.95   0.000     .2383284    .5506651
           suburb |   .3597642   .0668951     5.38   0.000     .2286522    .4908762
             town |   .1419935   .0827403     1.72   0.086    -.0201745    .3041616
            white |    .721201   .1141257     6.32   0.000     .4975187    .9448832
            black |   .6723839    .157213     4.28   0.000      .364252    .9805158
    race_hispanic |   .8233291   .1425366     5.78   0.000     .5439626    1.102696
            asian |   1.355853   .2470049     5.49   0.000     .8717328    1.839974
            _cons |   -.003324   .6310291    -0.01   0.996    -1.240118     1.23347
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .2255755   .0441124      .1537571    .3309396
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 26.25       Prob >= chibar2 = 0.0000
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share female age covid_di
> agnosed hs college city suburb town white black race_hispanic asian || czone: if ideo5 == 4 | ideo5 == 
> 5

Refining starting values: 

Iteration 0:   log likelihood = -4576.7691  
Iteration 1:   log likelihood = -4530.5689  
Iteration 2:   log likelihood = -4498.9772  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -4498.9772  
Iteration 1:   log likelihood = -4495.4196  
Iteration 2:   log likelihood = -4494.7466  
Iteration 3:   log likelihood = -4494.7465  

Mixed-effects logistic regression               Number of obs     =      7,315
Group variable: czone                           Number of groups  =        557

                                                Obs per group:
                                                              min =          1
                                                              avg =       13.1
                                                              max =        242

Integration points =   7                        Wald chi2(16)     =     720.01
Log likelihood = -4494.7465                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.1488725   .0507466    -2.93   0.003    -.2483339   -.0494111
            ideo7 |  -.2420594   .0400311    -6.05   0.000     -.320519   -.1635999
cz_ideo7_mean2021 |  -.2337212   .0733211    -3.19   0.001    -.3774278   -.0900145
     cz_imm_share |   .9269912   .3534303     2.62   0.009     .2342806    1.619702
           female |  -.2626229   .0516929    -5.08   0.000    -.3639391   -.1613067
              age |   .0334947   .0016951    19.76   0.000     .0301724     .036817
  covid_diagnosed |   .4418992   .0574532     7.69   0.000     .3292931    .5545054
               hs |   .3051421   .1430531     2.13   0.033     .0247632     .585521
          college |   .8204118   .1491107     5.50   0.000     .5281602    1.112663
             city |   .3790109   .0806504     4.70   0.000      .220939    .5370828
           suburb |   .3491069   .0688913     5.07   0.000     .2140824    .4841314
             town |   .1390123   .0832302     1.67   0.095    -.0241159    .3021405
            white |   .7371069    .114718     6.43   0.000     .5122638      .96195
            black |   .6874959   .1577227     4.36   0.000     .3783651    .9966266
    race_hispanic |   .8173824   .1433737     5.70   0.000     .5363751     1.09839
            asian |    1.35778   .2471644     5.49   0.000      .873347    1.842214
            _cons |  -.4104442   .3688227    -1.11   0.266    -1.133323     .312435
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |     .24755   .0434141      .1755429    .3490941
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 20.20       Prob >= chibar2 = 0.0000
(est6 stored)

. 
. esttab using "TableA4.tex", label keep(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_m
> ean2021 ideo7 cz_imm_share female age covid_diagnosed hs college city suburb town white black race_hisp
> anic asian covid_diagnosed) order(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_mean20
> 21 ideo7 cz_imm_share female age covid_diagnosed hs college city suburb town white black race_hispanic 
> asian covid_diagnosed) margin nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps no
> depvars 
(output written to TableA4.tex)

. 
. * Table A5: Results: US-Born Respondents Only
. 
. eststo clear

. 
. eststo: regress vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share female age covid_diag
> nosed hs college income_50k income_100k city suburb town white black race_hispanic asian if immigrant =
> = 0, cluster(inputstate)

Linear regression                               Number of obs     =     19,904
                                                F(18, 48)         =     140.46
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1912
                                                Root MSE          =     .38537

                                 (Std. Err. adjusted for 49 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0217039   .0027685    -7.84   0.000    -.0272704   -.0161375
            ideo7 |  -.0711143    .002088   -34.06   0.000    -.0753125   -.0669162
st_ideo7_mean2021 |  -.0461343    .013363    -3.45   0.001    -.0730025   -.0192661
     cz_imm_share |   .0806829   .0406384     1.99   0.053    -.0010262     .162392
           female |  -.0330767   .0066802    -4.95   0.000    -.0465081   -.0196452
              age |   .0052492   .0002686    19.55   0.000     .0047092    .0057892
  covid_diagnosed |   .0766002   .0086867     8.82   0.000     .0591343     .094066
               hs |   .1374262   .0207478     6.62   0.000     .0957098    .1791425
          college |   .2234612   .0202335    11.04   0.000     .1827791    .2641433
       income_50k |   .0560113   .0069654     8.04   0.000     .0420065     .070016
      income_100k |   .0700188   .0075533     9.27   0.000     .0548319    .0852057
             city |    .075249    .009764     7.71   0.000     .0556172    .0948809
           suburb |   .0778132   .0080488     9.67   0.000       .06163    .0939964
             town |   .0384081   .0104928     3.66   0.001     .0173109    .0595053
            white |    .092687   .0134501     6.89   0.000     .0656438    .1197303
            black |   .0474009   .0151822     3.12   0.003     .0168749    .0779268
    race_hispanic |   .0762485   .0147584     5.17   0.000     .0465748    .1059222
            asian |   .1774383   .0272857     6.50   0.000     .1225767    .2322999
            _cons |   .5960096   .0584219    10.20   0.000     .4785445    .7134747
-----------------------------------------------------------------------------------
(est1 stored)

. eststo: areg  vaccinated st_ideo7_dist2021 ideo7 cz_imm_share female age covid_diagnosed hs college inc
> ome_50k income_100k city suburb town white black race_hispanic asian if immigrant == 0, cluster(inputst
> ate) absorb(inputstate)

Linear regression, absorbing indicators         Number of obs     =     19,904
Absorbed variable: inputstate                   No. of categories =         49
                                                F(  17,     48)   =     115.23
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1947
                                                Adj R-squared     =     0.1921
                                                Root MSE          =     0.3850

                                 (Std. Err. adjusted for 49 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |   -.021467   .0027236    -7.88   0.000     -.026943   -.0159909
            ideo7 |  -.0711343   .0020818   -34.17   0.000    -.0753199   -.0669486
     cz_imm_share |   .1522198   .0441005     3.45   0.001     .0635497    .2408899
           female |  -.0330167   .0067246    -4.91   0.000    -.0465374   -.0194961
              age |   .0053059   .0002677    19.82   0.000     .0047676    .0058442
  covid_diagnosed |   .0769956   .0086467     8.90   0.000     .0596102    .0943811
               hs |   .1381799    .020697     6.68   0.000     .0965658    .1797939
          college |   .2224573   .0205076    10.85   0.000     .1812241    .2636906
       income_50k |   .0559377     .00711     7.87   0.000     .0416422    .0702333
      income_100k |   .0675578   .0074907     9.02   0.000     .0524967    .0826188
             city |   .0778765   .0100969     7.71   0.000     .0575754    .0981777
           suburb |   .0780797   .0079543     9.82   0.000     .0620864     .094073
             town |   .0370028   .0105101     3.52   0.001     .0158709    .0581347
            white |   .0908068   .0136866     6.63   0.000      .063288    .1183257
            black |    .039371   .0151363     2.60   0.012     .0089375    .0698046
    race_hispanic |    .080829   .0163739     4.94   0.000      .047907     .113751
            asian |   .1786386   .0269097     6.64   0.000     .1245331    .2327442
            _cons |   .4030659   .0369892    10.90   0.000     .3286941    .4774376
-----------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: regress vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share female age covid_diag
> nosed hs college income_50k income_100k city suburb town white black race_hispanic asian if immigrant =
> = 0, cluster(czone)

Linear regression                               Number of obs     =     19,904
                                                F(18, 618)        =     120.82
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1912
                                                Root MSE          =     .38538

                                     (Std. Err. adjusted for 619 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0198922   .0027874    -7.14   0.000    -.0253662   -.0144183
            ideo7 |  -.0699412   .0022602   -30.94   0.000    -.0743798   -.0655026
cz_ideo7_mean2021 |  -.0370783   .0074078    -5.01   0.000    -.0516258   -.0225308
     cz_imm_share |   .0743041   .0385566     1.93   0.054    -.0014137     .150022
           female |  -.0330179   .0058095    -5.68   0.000    -.0444268   -.0216091
              age |   .0052352   .0002306    22.71   0.000     .0047824    .0056879
  covid_diagnosed |   .0761474   .0072952    10.44   0.000      .061821    .0904738
               hs |   .1379634    .019089     7.23   0.000     .1004762    .1754507
          college |   .2222053   .0194859    11.40   0.000     .1839387    .2604718
       income_50k |   .0551802   .0071567     7.71   0.000     .0411259    .0692345
      income_100k |   .0686827   .0085586     8.03   0.000     .0518753    .0854901
             city |   .0707556   .0098704     7.17   0.000     .0513721    .0901391
           suburb |   .0728803   .0088554     8.23   0.000     .0554899    .0902706
             town |   .0372784    .010881     3.43   0.001       .01591    .0586467
            white |     .09253   .0142591     6.49   0.000     .0645278    .1205321
            black |   .0453832    .016065     2.82   0.005     .0138345    .0769318
    race_hispanic |   .0777558   .0175537     4.43   0.000     .0432838    .1122279
            asian |   .1798708   .0228933     7.86   0.000     .1349128    .2248288
            _cons |   .5589535   .0416225    13.43   0.000     .4772149    .6406921
-----------------------------------------------------------------------------------
(est3 stored)

. eststo: areg vaccinated cz_ideo7_dist2021 ideo7 female age covid_diagnosed hs college income_50k income
> _100k city suburb town white black race_hispanic asian if immigrant == 0, cluster(czone) absorb(czone)

Linear regression, absorbing indicators         Number of obs     =     19,904
Absorbed variable: czone                        No. of categories =        619
                                                F(  16,    618)   =      89.64
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2266
                                                Adj R-squared     =     0.2011
                                                Root MSE          =     0.3828

                                     (Std. Err. adjusted for 619 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0196011   .0028653    -6.84   0.000    -.0252279   -.0139743
            ideo7 |  -.0700574   .0022954   -30.52   0.000    -.0745652   -.0655497
           female |  -.0318136   .0059915    -5.31   0.000    -.0435799   -.0200474
              age |   .0051958   .0002371    21.91   0.000     .0047301    .0056615
  covid_diagnosed |   .0770529    .007487    10.29   0.000     .0623498     .091756
               hs |   .1373158   .0195791     7.01   0.000     .0988661    .1757655
          college |   .2194476   .0200197    10.96   0.000     .1801328    .2587624
       income_50k |   .0520184   .0073452     7.08   0.000     .0375937     .066443
      income_100k |   .0629682   .0088418     7.12   0.000     .0456045    .0803318
             city |   .0656588   .0110507     5.94   0.000     .0439572    .0873603
           suburb |   .0659211   .0102913     6.41   0.000      .045711    .0861312
             town |   .0323367   .0116536     2.77   0.006     .0094512    .0552223
            white |   .0895694   .0149476     5.99   0.000     .0602152    .1189236
            black |   .0362447   .0162825     2.23   0.026     .0042689    .0682205
    race_hispanic |   .0803052   .0182105     4.41   0.000     .0445433    .1160671
            asian |      .1769   .0228466     7.74   0.000     .1320337    .2217663
            _cons |   .4368755   .0287929    15.17   0.000     .3803318    .4934192
-----------------------------------------------------------------------------------
(est4 stored)

. 
. eststo: xtmelogit vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share female age covid_di
> agnosed hs college income_50k income_100k city suburb town white black race_hispanic asian || inputstat
> e: if immigrant == 0

Refining starting values: 

Iteration 0:   log likelihood = -8971.4642  (not concave)
Iteration 1:   log likelihood = -8970.7177  (not concave)
Iteration 2:   log likelihood = -8943.8686  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -8943.8686  (not concave)
Iteration 1:   log likelihood = -8919.2176  
Iteration 2:   log likelihood = -8917.3254  
Iteration 3:   log likelihood = -8917.1701  
Iteration 4:   log likelihood =   -8917.17  

Mixed-effects logistic regression               Number of obs     =     19,904
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         33
                                                              avg =      406.2
                                                              max =      1,705

Integration points =   7                        Wald chi2(18)     =    2913.65
Log likelihood =   -8917.17                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0279455   .0181779    -1.54   0.124    -.0635735    .0076826
            ideo7 |  -.4877691   .0121121   -40.27   0.000    -.5115084   -.4640298
st_ideo7_mean2021 |  -.2117592   .1006922    -2.10   0.035    -.4091123   -.0144061
     cz_imm_share |   1.028497   .2467037     4.17   0.000     .5449665    1.512027
           female |   -.195154    .038387    -5.08   0.000     -.270391   -.1199169
              age |   .0359764    .001214    29.64   0.000     .0335971    .0383558
  covid_diagnosed |   .4869642   .0414242    11.76   0.000     .4057743    .5681542
               hs |   .6566716   .0915947     7.17   0.000     .4771492    .8361939
          college |   1.327661    .098632    13.46   0.000     1.134346    1.520976
       income_50k |   .3691217   .0434059     8.50   0.000     .2840477    .4541956
      income_100k |   .5220447   .0583012     8.95   0.000     .4077764     .636313
             city |   .4543003   .0562114     8.08   0.000      .344128    .5644726
           suburb |   .4407934   .0513972     8.58   0.000     .3400567    .5415301
             town |   .1920862    .062355     3.08   0.002     .0698727    .3142996
            white |   .5896944   .0776768     7.59   0.000     .4374507    .7419381
            black |   .2206563   .0920658     2.40   0.017     .0402107    .4011018
    race_hispanic |   .4703339   .0954737     4.93   0.000     .2832088    .6574589
            asian |   1.307739    .209678     6.24   0.000     .8967775      1.7187
            _cons |   .1176522     .42852     0.27   0.784    -.7222315     .957536
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .1349921   .0299895      .0873388    .2086456
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 15.64       Prob >= chibar2 = 0.0000
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share female age covid_di
> agnosed hs college income_50k income_100k city suburb town white black race_hispanic asian || czone: if
>  immigrant == 0

Refining starting values: 

Iteration 0:   log likelihood = -9051.8317  (not concave)
Iteration 1:   log likelihood =  -8963.297  
Iteration 2:   log likelihood = -8913.1599  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -8913.1599  
Iteration 1:   log likelihood = -8909.8415  
Iteration 2:   log likelihood = -8909.7489  
Iteration 3:   log likelihood = -8909.7489  

Mixed-effects logistic regression               Number of obs     =     19,904
Group variable: czone                           Number of groups  =        619

                                                Obs per group:
                                                              min =          1
                                                              avg =       32.2
                                                              max =        784

Integration points =   7                        Wald chi2(18)     =    2891.99
Log likelihood = -8909.7489                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0332681   .0187085    -1.78   0.075    -.0699361       .0034
            ideo7 |  -.4835749   .0122268   -39.55   0.000    -.5075389   -.4596108
cz_ideo7_mean2021 |   -.159163   .0463099    -3.44   0.001    -.2499288   -.0683973
     cz_imm_share |   .8233153   .2611892     3.15   0.002     .3113939    1.335237
           female |  -.1962413   .0385181    -5.09   0.000    -.2717354   -.1207472
              age |   .0359978   .0012162    29.60   0.000     .0336141    .0383816
  covid_diagnosed |   .4894091   .0415779    11.77   0.000     .4079179    .5709002
               hs |   .6613229   .0919168     7.19   0.000     .4811692    .8414766
          college |   1.332116    .099003    13.46   0.000     1.138073    1.526158
       income_50k |   .3620115   .0435639     8.31   0.000     .2766279    .4473951
      income_100k |   .5086866   .0585683     8.69   0.000     .3938947    .6234784
             city |   .4263108   .0569661     7.48   0.000     .3146594    .5379623
           suburb |    .409903   .0526018     7.79   0.000     .3068053    .5130007
             town |   .1871407   .0626641     2.99   0.003     .0643213    .3099601
            white |   .5944376   .0779415     7.63   0.000     .4416752    .7472001
            black |   .2197326   .0920805     2.39   0.017     .0392582     .400207
    race_hispanic |    .463695   .0956499     4.85   0.000     .2762246    .6511655
            asian |   1.321531   .2103361     6.28   0.000       .90928    1.733782
            _cons |  -.0750738   .2407878    -0.31   0.755    -.5470093    .3968616
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |    .186211   .0319357      .1330521    .2606087
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 21.35       Prob >= chibar2 = 0.0000
(est6 stored)

. 
. esttab using "TableA5.tex", label keep(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_m
> ean2021 ideo7 cz_imm_share female age covid_diagnosed hs college income_50k income_100k city suburb tow
> n white black race_hispanic asian covid_diagnosed) order(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_d
> ist2021 cz_ideo7_mean2021 ideo7 cz_imm_share female age covid_diagnosed hs college income_50k income_10
> 0k city suburb town white black race_hispanic asian covid_diagnosed) margin nonotes se(3) b(3) replace 
> star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars 
(output written to TableA5.tex)

. 
. * Table A6: Results: Comparing White versus Non-White Immigrants
. 
. eststo clear

. 
. eststo: regress vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share_white female age covi
> d_diagnosed hs college city suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, clu
> ster(inputstate)

Linear regression                               Number of obs     =      7,315
                                                F(16, 48)         =     129.06
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1131
                                                Root MSE          =     .46322

                                  (Std. Err. adjusted for 49 clusters in inputstate)
------------------------------------------------------------------------------------
                   |               Robust
        vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
 st_ideo7_dist2021 |  -.0326815    .011389    -2.87   0.006    -.0555807   -.0097823
             ideo7 |   -.051046   .0074352    -6.87   0.000    -.0659954   -.0360965
 st_ideo7_mean2021 |  -.0944624   .0323913    -2.92   0.005    -.1595894   -.0293354
cz_imm_share_white |   .1995619   .1163082     1.72   0.093    -.0342915    .4334153
            female |  -.0569185   .0111357    -5.11   0.000    -.0793083   -.0345287
               age |   .0071413   .0003767    18.96   0.000      .006384    .0078986
   covid_diagnosed |   .0935071   .0138823     6.74   0.000     .0655948    .1214194
                hs |   .0636859   .0352983     1.80   0.077    -.0072861    .1346579
           college |   .1737103   .0352832     4.92   0.000     .1027687    .2446518
              city |   .0902286   .0160892     5.61   0.000      .057879    .1225782
            suburb |    .085243    .015086     5.65   0.000     .0549104    .1155755
              town |     .03652   .0189156     1.93   0.059    -.0015124    .0745524
             white |   .1627509   .0289214     5.63   0.000     .1046006    .2209013
             black |   .1622479   .0381034     4.26   0.000     .0856359    .2388599
     race_hispanic |   .1786894   .0416995     4.29   0.000     .0948469    .2625319
             asian |   .2845751   .0354604     8.03   0.000     .2132772    .3558731
             _cons |   .5891197   .1178858     5.00   0.000     .3520944    .8261451
------------------------------------------------------------------------------------
(est1 stored)

. eststo: areg  vaccinated st_ideo7_dist2021 ideo7 cz_imm_share_white female age covid_diagnosed hs colle
> ge city suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(inputstate) abs
> orb(inputstate)

Linear regression, absorbing indicators         Number of obs     =      7,315
Absorbed variable: inputstate                   No. of categories =         49
                                                F(  15,     48)   =      99.09
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1269
                                                Adj R-squared     =     0.1193
                                                Root MSE          =     0.4611

                                  (Std. Err. adjusted for 49 clusters in inputstate)
------------------------------------------------------------------------------------
                   |               Robust
        vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
 st_ideo7_dist2021 |  -.0301749   .0112234    -2.69   0.010    -.0527411   -.0076086
             ideo7 |  -.0520173   .0075879    -6.86   0.000    -.0672737   -.0367609
cz_imm_share_white |   .4685282   .1137259     4.12   0.000     .2398669    .6971895
            female |   -.055982   .0112253    -4.99   0.000     -.078552    -.033412
               age |   .0073019   .0003877    18.83   0.000     .0065224    .0080815
   covid_diagnosed |   .0954474   .0135431     7.05   0.000     .0682171    .1226777
                hs |   .0678596   .0351242     1.93   0.059    -.0027623    .1384814
           college |   .1734977   .0357236     4.86   0.000     .1016706    .2453249
              city |   .0942417   .0176256     5.35   0.000     .0588031    .1296804
            suburb |   .0854859   .0151215     5.65   0.000      .055082    .1158898
              town |   .0314453   .0185755     1.69   0.097    -.0059032    .0687938
             white |   .1562475   .0294356     5.31   0.000     .0970634    .2154317
             black |   .1454746   .0394945     3.68   0.001     .0660656    .2248837
     race_hispanic |   .1829573   .0429608     4.26   0.000     .0965789    .2693357
             asian |   .2837093   .0324308     8.75   0.000     .2185028    .3489158
             _cons |   .1903585     .07261     2.62   0.012     .0443663    .3363507
------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: regress vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share_white female age covi
> d_diagnosed hs college city suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, clu
> ster(czone)

Linear regression                               Number of obs     =      7,315
                                                F(16, 556)        =      51.19
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1132
                                                Root MSE          =     .46319

                                      (Std. Err. adjusted for 557 clusters in czone)
------------------------------------------------------------------------------------
                   |               Robust
        vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
 cz_ideo7_dist2021 |  -.0322488   .0108958    -2.96   0.003    -.0536508   -.0108468
             ideo7 |  -.0505289   .0080969    -6.24   0.000    -.0664331   -.0346247
 cz_ideo7_mean2021 |  -.0662777   .0171716    -3.86   0.000    -.1000068   -.0325486
cz_imm_share_white |    .196503   .1106561     1.78   0.076    -.0208522    .4138581
            female |  -.0573547   .0108781    -5.27   0.000    -.0787218   -.0359875
               age |   .0071544   .0003964    18.05   0.000     .0063758     .007933
   covid_diagnosed |   .0938119   .0124782     7.52   0.000     .0693018    .1183221
                hs |   .0665193    .030453     2.18   0.029     .0067022    .1263363
           college |   .1758009   .0320069     5.49   0.000     .1129317    .2386701
              city |   .0837447   .0188539     4.44   0.000     .0467111    .1207782
            suburb |   .0769525   .0147203     5.23   0.000     .0480383    .1058666
              town |   .0359329   .0193372     1.86   0.064    -.0020501    .0739158
             white |   .1613842   .0246184     6.56   0.000     .1130278    .2097406
             black |   .1572561   .0338019     4.65   0.000     .0908611     .223651
     race_hispanic |   .1773302   .0317242     5.59   0.000     .1150162    .2396443
             asian |   .2884855   .0466127     6.19   0.000     .1969271     .380044
             _cons |   .4805004   .0811631     5.92   0.000     .3210766    .6399242
------------------------------------------------------------------------------------
(est3 stored)

. eststo: areg vaccinated cz_ideo7_dist2021 ideo7 female age covid_diagnosed hs college city suburb town 
> white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(czone) absorb(czone)

Linear regression, absorbing indicators         Number of obs     =      7,315
Absorbed variable: czone                        No. of categories =        557
                                                F(  14,    556)   =      45.08
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1964
                                                Adj R-squared     =     0.1285
                                                Root MSE          =     0.4587

                                     (Std. Err. adjusted for 557 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |   -.025675   .0111429    -2.30   0.022    -.0475623   -.0037877
            ideo7 |  -.0521732   .0081952    -6.37   0.000    -.0682706   -.0360758
           female |  -.0492709    .011899    -4.14   0.000    -.0726433   -.0258984
              age |   .0072742   .0004301    16.91   0.000     .0064293    .0081191
  covid_diagnosed |   .0965521   .0136566     7.07   0.000     .0697272    .1233769
               hs |   .0670122   .0335322     2.00   0.046     .0011469    .1328776
          college |   .1771335   .0355304     4.99   0.000     .1073433    .2469237
             city |   .0859984   .0218543     3.94   0.000     .0430713    .1289255
           suburb |   .0728039   .0184424     3.95   0.000     .0365786    .1090292
             town |   .0267559   .0216822     1.23   0.218    -.0158332    .0693449
            white |   .1602474   .0267115     6.00   0.000     .1077796    .2127152
            black |   .1357828   .0360782     3.76   0.000     .0649166    .2066491
    race_hispanic |   .1838527   .0339442     5.42   0.000     .1171781    .2505273
            asian |    .274344   .0460681     5.96   0.000     .1838552    .3648328
            _cons |   .2108333   .0613991     3.43   0.001     .0902308    .3314358
-----------------------------------------------------------------------------------
(est4 stored)

. 
. eststo: xtmelogit vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share_white female age co
> vid_diagnosed hs college city suburb town white black race_hispanic asian || inputstate: if ideo5 == 4 
> | ideo5 == 5

Refining starting values: 

Iteration 0:   log likelihood = -4531.3926  (not concave)
Iteration 1:   log likelihood = -4506.6504  
Iteration 2:   log likelihood =  -4497.297  

Performing gradient-based optimization: 

Iteration 0:   log likelihood =  -4497.297  
Iteration 1:   log likelihood = -4495.2396  
Iteration 2:   log likelihood = -4495.2182  
Iteration 3:   log likelihood = -4495.2182  

Mixed-effects logistic regression               Number of obs     =      7,315
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         11
                                                              avg =      149.3
                                                              max =        609

Integration points =   7                        Wald chi2(16)     =     724.18
Log likelihood = -4495.2182                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------------
        vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
 st_ideo7_dist2021 |  -.1478337   .0494893    -2.99   0.003     -.244831   -.0508363
             ideo7 |  -.2424484   .0394394    -6.15   0.000    -.3197482   -.1651487
 st_ideo7_mean2021 |  -.4234286   .1441965    -2.94   0.003    -.7060486   -.1408087
cz_imm_share_white |   1.886558   .5917704     3.19   0.001      .726709    3.046406
            female |  -.2633101   .0514405    -5.12   0.000    -.3641317   -.1624886
               age |   .0334972   .0016848    19.88   0.000     .0301951    .0367994
   covid_diagnosed |   .4377615   .0570534     7.67   0.000     .3259389    .5495842
                hs |   .2944596   .1421425     2.07   0.038     .0158655    .5730538
           college |   .8107605   .1480991     5.47   0.000     .5204915    1.101029
              city |    .419503   .0790804     5.30   0.000     .2645083    .5744976
            suburb |   .3856295   .0661627     5.83   0.000     .2559529     .515306
              town |   .1435045   .0827439     1.73   0.083    -.0186705    .3056795
             white |   .7161073   .1140613     6.28   0.000     .4925513    .9396632
             black |   .6816992   .1570655     4.34   0.000     .3738565    .9895419
     race_hispanic |   .8265728    .142635     5.80   0.000     .5470134    1.106132
             asian |   1.397541   .2462969     5.67   0.000      .914808    1.880274
             _cons |   .3838796    .613305     0.63   0.531    -.8181761    1.585935
------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .2241864   .0444504      .1519979    .3306592
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 24.59       Prob >= chibar2 = 0.0000
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share_white female age co
> vid_diagnosed hs college city suburb town white black race_hispanic asian || czone: if ideo5 == 4 | ide
> o5 == 5

Refining starting values: 

Iteration 0:   log likelihood = -4577.1702  
Iteration 1:   log likelihood = -4518.5371  
Iteration 2:   log likelihood = -4497.8208  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -4497.8208  
Iteration 1:   log likelihood = -4496.6034  
Iteration 2:   log likelihood = -4496.6024  
Iteration 3:   log likelihood = -4496.6024  

Mixed-effects logistic regression               Number of obs     =      7,315
Group variable: czone                           Number of groups  =        557

                                                Obs per group:
                                                              min =          1
                                                              avg =       13.1
                                                              max =        242

Integration points =   7                        Wald chi2(16)     =     717.95
Log likelihood = -4496.6024                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------------
        vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
 cz_ideo7_dist2021 |  -.1486994   .0507638    -2.93   0.003    -.2481946   -.0492042
             ideo7 |  -.2419655   .0400464    -6.04   0.000     -.320455    -.163476
 cz_ideo7_mean2021 |  -.2685867   .0714105    -3.76   0.000    -.4085487   -.1286246
cz_imm_share_white |   1.055011   .5995583     1.76   0.078    -.1201017    2.230124
            female |  -.2639456   .0517048    -5.10   0.000    -.3652853    -.162606
               age |   .0335607   .0016955    19.79   0.000     .0302376    .0368838
   covid_diagnosed |   .4397537   .0574599     7.65   0.000     .3271344     .552373
                hs |   .3070059   .1431256     2.15   0.032     .0264848    .5875269
           college |   .8257573   .1491571     5.54   0.000     .5334147      1.1181
              city |   .3902527   .0805853     4.84   0.000     .2323083     .548197
            suburb |   .3595384   .0688196     5.22   0.000     .2246545    .4944222
              town |   .1406532     .08331     1.69   0.091    -.0226313    .3039377
             white |   .7311923   .1147058     6.37   0.000      .506373    .9560117
             black |   .6849229   .1577598     4.34   0.000     .3757193    .9941265
     race_hispanic |   .8247202   .1436744     5.74   0.000     .5431236    1.106317
             asian |   1.390567    .246644     5.64   0.000     .9071539    1.873981
             _cons |  -.2246925    .357166    -0.63   0.529     -.924725      .47534
------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .2551787    .043706      .1824122    .3569726
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 21.19       Prob >= chibar2 = 0.0000
(est6 stored)

. 
. esttab using "TableA6.tex", label keep(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_m
> ean2021 ideo7 cz_imm_share_white female age covid_diagnosed hs college city suburb town white black rac
> e_hispanic asian covid_diagnosed) order(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_
> mean2021 ideo7 cz_imm_share_white female age covid_diagnosed hs college city suburb town white black ra
> ce_hispanic asian covid_diagnosed) margin nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compre
> ss nogaps nodepvars 
(output written to TableA6.tex)

. 
. * Table A7: Results: Comparing White versus Non-White Immigrants (Continued)
. 
. eststo clear

. 
. eststo: regress vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share_nonwhite female age c
> ovid_diagnosed hs college city suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, 
> cluster(inputstate)

Linear regression                               Number of obs     =      7,315
                                                F(16, 48)         =     110.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1137
                                                Root MSE          =     .46306

                                     (Std. Err. adjusted for 49 clusters in inputstate)
---------------------------------------------------------------------------------------
                      |               Robust
           vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
    st_ideo7_dist2021 |  -.0323737   .0112578    -2.88   0.006     -.055009   -.0097383
                ideo7 |  -.0509115   .0073674    -6.91   0.000    -.0657248   -.0360983
    st_ideo7_mean2021 |  -.0710894    .029793    -2.39   0.021    -.1309923   -.0111866
cz_imm_share_nonwhite |   .2991215   .1688725     1.77   0.083    -.0404194    .6386624
               female |  -.0563485   .0111863    -5.04   0.000    -.0788401   -.0338568
                  age |   .0071452   .0003804    18.78   0.000     .0063804      .00791
      covid_diagnosed |   .0946775   .0139622     6.78   0.000     .0666045    .1227504
                   hs |   .0640665   .0351859     1.82   0.075    -.0066794    .1348124
              college |   .1725685   .0352006     4.90   0.000      .101793     .243344
                 city |   .0862643   .0166561     5.18   0.000     .0527749    .1197537
               suburb |   .0814231   .0154714     5.26   0.000     .0503158    .1125305
                 town |   .0367336   .0188424     1.95   0.057    -.0011516    .0746188
                white |   .1641759   .0286625     5.73   0.000      .106546    .2218058
                black |   .1608132   .0380434     4.23   0.000     .0843219    .2373045
        race_hispanic |   .1814743   .0425298     4.27   0.000     .0959624    .2669861
                asian |   .2717294   .0365474     7.43   0.000     .1982459     .345213
                _cons |   .4859652   .1128201     4.31   0.000     .2591252    .7128052
---------------------------------------------------------------------------------------
(est1 stored)

. eststo: areg  vaccinated st_ideo7_dist2021 ideo7 cz_imm_share_nonwhite female age covid_diagnosed hs co
> llege city suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(inputstate) 
> absorb(inputstate)

Linear regression, absorbing indicators         Number of obs     =      7,315
Absorbed variable: inputstate                   No. of categories =         49
                                                F(  15,     48)   =      94.28
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1271
                                                Adj R-squared     =     0.1195
                                                Root MSE          =     0.4610

                                     (Std. Err. adjusted for 49 clusters in inputstate)
---------------------------------------------------------------------------------------
                      |               Robust
           vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
    st_ideo7_dist2021 |  -.0296889   .0112618    -2.64   0.011    -.0523322   -.0070456
                ideo7 |  -.0522417    .007483    -6.98   0.000    -.0672874   -.0371961
cz_imm_share_nonwhite |    .459907   .1222636     3.76   0.000     .2140797    .7057344
               female |  -.0557612   .0113146    -4.93   0.000    -.0785107   -.0330117
                  age |   .0072881   .0003967    18.37   0.000     .0064905    .0080856
      covid_diagnosed |   .0963077   .0134531     7.16   0.000     .0692585     .123357
                   hs |   .0681749   .0347464     1.96   0.056    -.0016875    .1380373
              college |   .1719076   .0355188     4.84   0.000     .1004923     .243323
                 city |   .0911738    .017951     5.08   0.000     .0550808    .1272667
               suburb |   .0813407   .0153614     5.30   0.000     .0504545    .1122268
                 town |   .0319752    .018422     1.74   0.089    -.0050647     .069015
                white |   .1560029   .0289952     5.38   0.000      .097704    .2143018
                black |   .1420715   .0388909     3.65   0.001     .0638761    .2202669
        race_hispanic |   .1873675   .0439134     4.27   0.000     .0990736    .2756614
                asian |   .2776172   .0329394     8.43   0.000     .2113881    .3438463
                _cons |     .18965   .0739703     2.56   0.014     .0409228    .3383772
---------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: regress vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share_nonwhite female age c
> ovid_diagnosed hs college city suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, 
> cluster(czone)

Linear regression                               Number of obs     =      7,315
                                                F(16, 556)        =      50.91
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1138
                                                Root MSE          =     .46304

                                         (Std. Err. adjusted for 557 clusters in czone)
---------------------------------------------------------------------------------------
                      |               Robust
           vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
    cz_ideo7_dist2021 |  -.0319289   .0108924    -2.93   0.004    -.0533243   -.0105335
                ideo7 |  -.0506284   .0080881    -6.26   0.000    -.0665154   -.0347414
    cz_ideo7_mean2021 |  -.0545729   .0171281    -3.19   0.002    -.0882165   -.0209293
cz_imm_share_nonwhite |    .289176   .1612795     1.79   0.074    -.0276156    .6059676
               female |  -.0566552   .0108591    -5.22   0.000    -.0779851   -.0353254
                  age |   .0071514   .0003943    18.14   0.000     .0063769    .0079258
      covid_diagnosed |    .094851   .0125573     7.55   0.000     .0701855    .1195165
                   hs |   .0662966   .0305726     2.17   0.031     .0062447    .1263484
              college |    .174386   .0321379     5.43   0.000     .1112593    .2375126
                 city |   .0820215   .0185855     4.41   0.000     .0455151    .1185278
               suburb |   .0758269   .0147662     5.14   0.000     .0468227    .1048312
                 town |   .0362333   .0192395     1.88   0.060    -.0015576    .0740243
                white |   .1632793   .0244939     6.67   0.000     .1151674    .2113911
                black |   .1572688   .0340308     4.62   0.000     .0904241    .2241134
        race_hispanic |   .1805302   .0319013     5.66   0.000     .1178683     .243192
                asian |   .2744984   .0470426     5.84   0.000     .1820956    .3669013
                _cons |   .4230916   .0787845     5.37   0.000       .26834    .5778433
---------------------------------------------------------------------------------------
(est3 stored)

. eststo: areg vaccinated cz_ideo7_dist2021 ideo7 female age covid_diagnosed hs college city suburb town 
> white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(czone) absorb(czone)

Linear regression, absorbing indicators         Number of obs     =      7,315
Absorbed variable: czone                        No. of categories =        557
                                                F(  14,    556)   =      45.08
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1964
                                                Adj R-squared     =     0.1285
                                                Root MSE          =     0.4587

                                     (Std. Err. adjusted for 557 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |   -.025675   .0111429    -2.30   0.022    -.0475623   -.0037877
            ideo7 |  -.0521732   .0081952    -6.37   0.000    -.0682706   -.0360758
           female |  -.0492709    .011899    -4.14   0.000    -.0726433   -.0258984
              age |   .0072742   .0004301    16.91   0.000     .0064293    .0081191
  covid_diagnosed |   .0965521   .0136566     7.07   0.000     .0697272    .1233769
               hs |   .0670122   .0335322     2.00   0.046     .0011469    .1328776
          college |   .1771335   .0355304     4.99   0.000     .1073433    .2469237
             city |   .0859984   .0218543     3.94   0.000     .0430713    .1289255
           suburb |   .0728039   .0184424     3.95   0.000     .0365786    .1090292
             town |   .0267559   .0216822     1.23   0.218    -.0158332    .0693449
            white |   .1602474   .0267115     6.00   0.000     .1077796    .2127152
            black |   .1357828   .0360782     3.76   0.000     .0649166    .2066491
    race_hispanic |   .1838527   .0339442     5.42   0.000     .1171781    .2505273
            asian |    .274344   .0460681     5.96   0.000     .1838552    .3648328
            _cons |   .2108333   .0613991     3.43   0.001     .0902308    .3314358
-----------------------------------------------------------------------------------
(est4 stored)

. 
. eststo: xtmelogit vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm_share_nonwhite female age
>  covid_diagnosed hs college city suburb town white black race_hispanic asian || inputstate: if ideo5 ==
>  4 | ideo5 == 5

Refining starting values: 

Iteration 0:   log likelihood = -4529.5162  (not concave)
Iteration 1:   log likelihood = -4503.9604  
Iteration 2:   log likelihood = -4495.0737  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -4495.0737  
Iteration 1:   log likelihood = -4493.5641  
Iteration 2:   log likelihood = -4493.5554  
Iteration 3:   log likelihood = -4493.5554  

Mixed-effects logistic regression               Number of obs     =      7,315
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         11
                                                              avg =      149.3
                                                              max =        609

Integration points =   7                        Wald chi2(16)     =     727.39
Log likelihood = -4493.5554                     Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------
           vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
    st_ideo7_dist2021 |  -.1452023     .04947    -2.94   0.003    -.2421617   -.0482429
                ideo7 |   -.242845   .0394369    -6.16   0.000    -.3201399   -.1655502
    st_ideo7_mean2021 |   -.343137   .1455581    -2.36   0.018    -.6284256   -.0578484
cz_imm_share_nonwhite |   2.003649   .5427314     3.69   0.000     .9399155    3.067383
               female |  -.2608688   .0514372    -5.07   0.000    -.3616838   -.1600537
                  age |    .033437    .001684    19.86   0.000     .0301364    .0367376
      covid_diagnosed |   .4429858   .0570958     7.76   0.000     .3310801    .5548915
                   hs |   .2969911   .1422201     2.09   0.037     .0182448    .5757374
              college |   .8044291   .1482139     5.43   0.000     .5139352    1.094923
                 city |   .4049361    .079426     5.10   0.000      .249264    .5606081
               suburb |   .3666451   .0667869     5.49   0.000     .2357452    .4975449
                 town |    .148236   .0826471     1.79   0.073    -.0137493    .3102214
                white |   .7212349   .1140691     6.32   0.000     .4976636    .9448062
                black |    .671623   .1571607     4.27   0.000     .3635937    .9796524
        race_hispanic |   .8486308    .142153     5.97   0.000      .570016    1.127246
                asian |   1.340164   .2474664     5.42   0.000     .8551391     1.82519
                _cons |   .0320795   .6227836     0.05   0.959    -1.188554    1.252713
---------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .2133341   .0436959      .1427953     .318718
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 22.37       Prob >= chibar2 = 0.0000
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm_share_nonwhite female age
>  covid_diagnosed hs college city suburb town white black race_hispanic asian || czone: if ideo5 == 4 | 
> ideo5 == 5

Refining starting values: 

Iteration 0:   log likelihood = -4576.5885  
Iteration 1:   log likelihood = -4529.5761  
Iteration 2:   log likelihood = -4496.4499  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -4496.4499  
Iteration 1:   log likelihood = -4494.5944  
Iteration 2:   log likelihood = -4494.5382  
Iteration 3:   log likelihood = -4494.5381  

Mixed-effects logistic regression               Number of obs     =      7,315
Group variable: czone                           Number of groups  =        557

                                                Obs per group:
                                                              min =          1
                                                              avg =       13.1
                                                              max =        242

Integration points =   7                        Wald chi2(16)     =     720.37
Log likelihood = -4494.5381                     Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------
           vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
    cz_ideo7_dist2021 |  -.1485589   .0507272    -2.93   0.003    -.2479824   -.0491355
                ideo7 |  -.2417522   .0400107    -6.04   0.000    -.3201716   -.1633327
    cz_ideo7_mean2021 |  -.2216988   .0744177    -2.98   0.003    -.3675547   -.0758428
cz_imm_share_nonwhite |   1.650404   .6109434     2.70   0.007     .4529768    2.847831
               female |  -.2614546   .0516908    -5.06   0.000    -.3627668   -.1601424
                  age |    .033514   .0016945    19.78   0.000     .0301928    .0368352
      covid_diagnosed |   .4431221   .0574599     7.71   0.000     .3305028    .5557414
                   hs |   .3069006   .1430804     2.14   0.032     .0264682     .587333
              college |   .8208029   .1491354     5.50   0.000     .5285029    1.113103
                 city |    .385108   .0803112     4.80   0.000     .2277009    .5425151
               suburb |   .3536327   .0686156     5.15   0.000     .2191486    .4881168
                 town |   .1431967   .0831694     1.72   0.085    -.0198122    .3062057
                white |   .7370638   .1147003     6.43   0.000     .5122554    .9618723
                black |   .6869321   .1577019     4.36   0.000      .377842    .9960223
        race_hispanic |    .834977   .1428652     5.84   0.000     .5549663    1.114988
                asian |   1.338876   .2478866     5.40   0.000     .8530277    1.824725
                _cons |  -.4631033   .3738657    -1.24   0.215    -1.195867      .26966
---------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .2458595   .0433054      .1740835    .3472295
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 20.10       Prob >= chibar2 = 0.0000
(est6 stored)

. 
. esttab using "TableA7.tex", label keep(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_m
> ean2021 ideo7 cz_imm_share_nonwhite female age covid_diagnosed hs college city suburb town white black 
> race_hispanic asian covid_diagnosed) order(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ide
> o7_mean2021 ideo7 cz_imm_share_nonwhite female age covid_diagnosed hs college city suburb town white bl
> ack race_hispanic asian covid_diagnosed) margin nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) 
> compress nogaps nodepvars 
(output written to TableA7.tex)

. 
. * Table A8: Results: Non-White Share as a Measure of Ethnic Diversity
. 
. eststo clear

. 
. eststo: regress vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 non_white female age covid_diagnos
> ed hs college city suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(inpu
> tstate)

Linear regression                               Number of obs     =      7,320
                                                F(16, 49)         =     115.98
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1152
                                                Root MSE          =     .46262

                                 (Std. Err. adjusted for 50 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0327669   .0112085    -2.92   0.005    -.0552912   -.0102426
            ideo7 |  -.0500394   .0073738    -6.79   0.000    -.0648575   -.0352212
st_ideo7_mean2021 |  -.0912941   .0340755    -2.68   0.010    -.1597714   -.0228167
        non_white |   .1785117   .0500065     3.57   0.001     .0780198    .2790035
           female |  -.0563823   .0112135    -5.03   0.000    -.0789168   -.0338479
              age |   .0071239   .0003805    18.72   0.000     .0063592    .0078886
  covid_diagnosed |   .0946684    .013795     6.86   0.000     .0669464    .1223905
               hs |   .0668984   .0355944     1.88   0.066    -.0046313     .138428
          college |   .1757321   .0358317     4.90   0.000     .1037256    .2477386
             city |   .0751953   .0156978     4.79   0.000     .0436493    .1067413
           suburb |   .0726853    .015376     4.73   0.000     .0417861    .1035845
             town |   .0362824   .0186477     1.95   0.057    -.0011915    .0737563
            white |   .1659353   .0287553     5.77   0.000     .1081492    .2237213
            black |   .1446168   .0385419     3.75   0.000      .067164    .2220696
    race_hispanic |   .1812936   .0421137     4.30   0.000      .096663    .2659243
            asian |   .2669838   .0335706     7.95   0.000     .1995211    .3344464
            _cons |   .5426959   .1257243     4.32   0.000     .2900434    .7953484
-----------------------------------------------------------------------------------
(est1 stored)

. eststo: areg  vaccinated st_ideo7_dist2021 ideo7 non_white female age covid_diagnosed hs college city s
> uburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(inputstate) absorb(input
> state)

Linear regression, absorbing indicators         Number of obs     =      7,320
Absorbed variable: inputstate                   No. of categories =         50
                                                F(  15,     49)   =      94.16
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1281
                                                Adj R-squared     =     0.1205
                                                Root MSE          =     0.4607

                                 (Std. Err. adjusted for 50 clusters in inputstate)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.0298944   .0112374    -2.66   0.011    -.0524768   -.0073119
            ideo7 |  -.0513054    .007519    -6.82   0.000    -.0664153   -.0361954
        non_white |   .2204243   .0491568     4.48   0.000       .12164    .3192086
           female |  -.0553456   .0113578    -4.87   0.000      -.07817   -.0325212
              age |   .0072641    .000393    18.48   0.000     .0064742    .0080539
  covid_diagnosed |   .0961556   .0136063     7.07   0.000     .0688129    .1234984
               hs |   .0707445   .0355177     1.99   0.052    -.0006309    .1421199
          college |    .175649   .0365131     4.81   0.000     .1022732    .2490249
             city |   .0776939   .0174744     4.45   0.000     .0425779      .11281
           suburb |    .072279   .0156103     4.63   0.000      .040909     .103649
             town |   .0299223   .0182617     1.64   0.108     -.006776    .0666205
            white |    .157944   .0291469     5.42   0.000     .0993711    .2165169
            black |    .127915   .0397606     3.22   0.002     .0480131    .2078168
    race_hispanic |   .1888232   .0441685     4.28   0.000     .1000634     .277583
            asian |   .2814222   .0330753     8.51   0.000     .2149548    .3478896
            _cons |   .1674566   .0762101     2.20   0.033     .0143066    .3206065
-----------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: regress vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 non_white female age covid_diagnos
> ed hs college city suburb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(czon
> e)

Linear regression                               Number of obs     =      7,315
                                                F(16, 556)        =      51.04
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1149
                                                Root MSE          =     .46276

                                     (Std. Err. adjusted for 557 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.0322435   .0108629    -2.97   0.003    -.0535809   -.0109061
            ideo7 |  -.0499202   .0080403    -6.21   0.000    -.0657134   -.0341271
cz_ideo7_mean2021 |  -.0599207   .0169607    -3.53   0.000    -.0932356   -.0266058
        non_white |   .1691079   .0443777     3.81   0.000     .0819396    .2562763
           female |  -.0570305   .0108934    -5.24   0.000    -.0784278   -.0356332
              age |   .0071488   .0003965    18.03   0.000       .00637    .0079275
  covid_diagnosed |   .0947091   .0124572     7.60   0.000     .0702402    .1191779
               hs |   .0693839   .0304934     2.28   0.023     .0094875    .1292802
          college |   .1778714   .0319947     5.56   0.000     .1150261    .2407166
             city |   .0713614   .0187205     3.81   0.000     .0345899    .1081328
           suburb |   .0669983   .0154132     4.35   0.000      .036723    .0972735
             town |   .0361774   .0192401     1.88   0.061    -.0016147    .0739696
            white |   .1641127    .024646     6.66   0.000      .115702    .2125234
            black |   .1406303   .0342207     4.11   0.000     .0734126     .207848
    race_hispanic |   .1805727   .0315133     5.73   0.000      .118673    .2424725
            asian |   .2729845   .0427895     6.38   0.000     .1889358    .3570333
            _cons |   .4234167    .080298     5.27   0.000     .2656922    .5811411
-----------------------------------------------------------------------------------
(est3 stored)

. eststo: areg vaccinated cz_ideo7_dist2021 ideo7 non_white female age covid_diagnosed hs college city su
> burb town white black race_hispanic asian if ideo5 == 4 | ideo5 == 5, cluster(czone) absorb(czone)

Linear regression, absorbing indicators         Number of obs     =      7,315
Absorbed variable: czone                        No. of categories =        557
                                                F(  15,    556)   =      42.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1974
                                                Adj R-squared     =     0.1294
                                                Root MSE          =     0.4584

                                     (Std. Err. adjusted for 557 clusters in czone)
-----------------------------------------------------------------------------------
                  |               Robust
       vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |    -.02518   .0110901    -2.27   0.024    -.0469636   -.0033963
            ideo7 |  -.0518855   .0081769    -6.35   0.000    -.0679468   -.0358241
        non_white |   .1791698    .062581     2.86   0.004     .0562458    .3020939
           female |  -.0491303   .0119375    -4.12   0.000    -.0725785   -.0256821
              age |   .0072604   .0004299    16.89   0.000      .006416    .0081049
  covid_diagnosed |   .0969681    .013678     7.09   0.000     .0701012    .1238349
               hs |   .0662479   .0335774     1.97   0.049     .0002939    .1322019
          college |   .1761731   .0356297     4.94   0.000     .1061878    .2461584
             city |   .0719706    .022427     3.21   0.001     .0279186    .1160227
           suburb |   .0635502    .019073     3.33   0.001     .0260862    .1010142
             town |   .0251448   .0217694     1.16   0.249    -.0176157    .0679052
            white |   .1610506   .0267106     6.03   0.000     .1085845    .2135166
            black |   .1258006   .0362722     3.47   0.001     .0545533    .1970478
    race_hispanic |    .181978   .0340704     5.34   0.000     .1150556    .2489004
            asian |   .2730364   .0458181     5.96   0.000     .1830387     .363034
            _cons |   .1711335   .0633927     2.70   0.007     .0466149     .295652
-----------------------------------------------------------------------------------
(est4 stored)

. 
. eststo: xtmelogit vaccinated st_ideo7_dist2021 ideo7 st_ideo7_mean2021 non_white female age covid_diagn
> osed hs college city suburb town white black race_hispanic asian || inputstate: if ideo5 == 4 | ideo5 =
> = 5

Refining starting values: 

Iteration 0:   log likelihood = -4527.3786  (not concave)
Iteration 1:   log likelihood =  -4500.762  
Iteration 2:   log likelihood = -4492.4858  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -4492.4858  
Iteration 1:   log likelihood = -4491.3076  
Iteration 2:   log likelihood = -4491.3044  
Iteration 3:   log likelihood = -4491.3044  

Mixed-effects logistic regression               Number of obs     =      7,320
Group variable: inputstate                      Number of groups  =         50

                                                Obs per group:
                                                              min =          4
                                                              avg =      146.4
                                                              max =        609

Integration points =   7                        Wald chi2(16)     =     736.20
Log likelihood = -4491.3044                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
st_ideo7_dist2021 |  -.1474474   .0495793    -2.97   0.003    -.2446211   -.0502737
            ideo7 |  -.2378252   .0395208    -6.02   0.000    -.3152845   -.1603658
st_ideo7_mean2021 |  -.4658245   .1364317    -3.41   0.001    -.7332257   -.1984233
        non_white |   .9602252   .2069527     4.64   0.000     .5546054    1.365845
           female |  -.2604155   .0514496    -5.06   0.000    -.3612549   -.1595761
              age |   .0333476   .0016838    19.81   0.000     .0300475    .0366477
  covid_diagnosed |   .4421508   .0570729     7.75   0.000       .33029    .5540115
               hs |   .3115475    .142279     2.19   0.029     .0326858    .5904092
          college |    .824154   .1481797     5.56   0.000     .5337271    1.114581
             city |   .3512951   .0813406     4.32   0.000     .1918705    .5107197
           suburb |     .32857    .068155     4.82   0.000     .1949886    .4621513
             town |   .1433459    .082705     1.73   0.083    -.0187528    .3054447
            white |      .7302   .1140219     6.40   0.000     .5067212    .9536788
            black |   .6025709   .1579596     3.81   0.000     .2929758     .912166
    race_hispanic |   .8533909   .1419466     6.01   0.000     .5751806    1.131601
            asian |   1.338294    .246711     5.42   0.000     .8547496    1.821839
            _cons |   .4089353   .5854278     0.70   0.485    -.7384821    1.556353
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |    .204406   .0438607      .1342288    .3112731
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 19.81       Prob >= chibar2 = 0.0000
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 non_white female age covid_diagn
> osed hs college city suburb town white black race_hispanic asian || czone: if ideo5 == 4 | ideo5 == 5

Refining starting values: 

Iteration 0:   log likelihood = -4571.2998  
Iteration 1:   log likelihood = -4517.7209  
Iteration 2:   log likelihood = -4490.8292  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -4490.8292  
Iteration 1:   log likelihood = -4489.9706  
Iteration 2:   log likelihood = -4489.9705  

Mixed-effects logistic regression               Number of obs     =      7,315
Group variable: czone                           Number of groups  =        557

                                                Obs per group:
                                                              min =          1
                                                              avg =       13.1
                                                              max =        242

Integration points =   7                        Wald chi2(16)     =     728.31
Log likelihood = -4489.9705                     Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
       vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
cz_ideo7_dist2021 |  -.1489652   .0508684    -2.93   0.003    -.2486655   -.0492648
            ideo7 |  -.2386937    .040139    -5.95   0.000    -.3173646   -.1600228
cz_ideo7_mean2021 |  -.2453299   .0712847    -3.44   0.001    -.3850453   -.1056144
        non_white |   .8443445   .2093712     4.03   0.000     .4339844    1.254704
           female |  -.2631659   .0517359    -5.09   0.000    -.3645664   -.1617654
              age |   .0335557   .0016961    19.78   0.000     .0302314      .03688
  covid_diagnosed |   .4433794   .0575008     7.71   0.000     .3306799    .5560788
               hs |   .3200426   .1432892     2.23   0.026     .0392009    .6008843
          college |   .8349109   .1492819     5.59   0.000     .5423238    1.127498
             city |   .3310335   .0821543     4.03   0.000      .170014     .492053
           suburb |   .3125091   .0699787     4.47   0.000     .1753533    .4496648
             town |   .1411688   .0832724     1.70   0.090    -.0220421    .3043797
            white |   .7415071   .1147099     6.46   0.000     .5166798    .9663344
            black |   .6109404   .1586657     3.85   0.000     .2999613    .9219195
    race_hispanic |   .8344835   .1428106     5.84   0.000     .5545799    1.114387
            asian |   1.337718     .24702     5.42   0.000     .8535679    1.821868
            _cons |  -.4673162    .361729    -1.29   0.196    -1.176292    .2416596
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .2517591   .0433595      .1796329    .3528454
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 21.03       Prob >= chibar2 = 0.0000
(est6 stored)

. 
. esttab using "TableA8.tex", label keep(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_m
> ean2021 ideo7 non_white female age covid_diagnosed hs college city suburb town white black race_hispani
> c asian covid_diagnosed) order(st_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_mean2021 
> ideo7 non_white female age covid_diagnosed hs college city suburb town white black race_hispanic asian 
> covid_diagnosed) margin nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvar
> s 
(output written to TableA8.tex)

. 
. * Table A9: Results: Interactive Effect of Ideological Distance and Affective Polarization
. 
. eststo clear

. eststo: xtmelogit vaccinated st_ideo7_dist2021 affective_polarization_12_16_20 st_dist_affective_12_16_
> 20 ideo7 cz_imm_share female age covid_diagnosed hs college  city suburb town white black race_hispanic
>  asian  || inputstate: 

Refining starting values: 

Iteration 0:   log likelihood = -10802.184  (not concave)
Iteration 1:   log likelihood = -10754.115  
Iteration 2:   log likelihood = -10752.239  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -10752.239  
Iteration 1:   log likelihood = -10750.558  
Iteration 2:   log likelihood = -10750.542  
Iteration 3:   log likelihood = -10750.542  

Mixed-effects logistic regression               Number of obs     =     23,908
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         34
                                                              avg =      487.9
                                                              max =      2,187

Integration points =   7                        Wald chi2(17)     =    3379.82
Log likelihood = -10750.542                     Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------------------
                     vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
              st_ideo7_dist2021 |   .3863321   .2598678     1.49   0.137    -.1229995    .8956637
affective_polarization_12_16_20 |   .4860229   1.222687     0.40   0.691    -1.910401    2.882446
     st_dist_affective_12_16_20 |    -.95522   .5794721    -1.65   0.099    -2.090964    .1805244
                          ideo7 |  -.4733803   .0108881   -43.48   0.000    -.4947205     -.45204
                   cz_imm_share |   1.458271   .2091834     6.97   0.000     1.048279    1.868263
                         female |  -.2049837   .0348131    -5.89   0.000     -.273216   -.1367513
                            age |   .0346887   .0010932    31.73   0.000      .032546    .0368314
                covid_diagnosed |    .526972    .037414    14.08   0.000      .453642    .6003021
                             hs |   .6878082   .0833533     8.25   0.000     .5244388    .8511776
                        college |   1.441453   .0877937    16.42   0.000     1.269381    1.613526
                           city |   .4364125   .0515238     8.47   0.000     .3354277    .5373973
                         suburb |   .4758329   .0472481    10.07   0.000     .3832283    .5684375
                           town |   .1851532   .0574857     3.22   0.001     .0724832    .2978231
                          white |    .578538   .0690234     8.38   0.000     .4432546    .7138213
                          black |   .1645912   .0823781     2.00   0.046     .0031331    .3260493
                  race_hispanic |   .5383567   .0831624     6.47   0.000     .3753614    .7013521
                          asian |   1.325279   .1471762     9.00   0.000     1.036819    1.613739
                          _cons |  -.8343194   .5597549    -1.49   0.136    -1.931419      .26278
-------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .1516079   .0287959      .1044835    .2199865
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 30.11       Prob >= chibar2 = 0.0000
(est1 stored)

. eststo: xtmelogit vaccinated st_ideo7_dist2021 affective_polarization_12_16_20 st_dist_affective_12_16_
> 20 ideo7 cz_imm_share female age covid_diagnosed hs college  city suburb town white black race_hispanic
>  asian income_100k income_50k || inputstate: 

Refining starting values: 

Iteration 0:   log likelihood = -9752.1416  (not concave)
Iteration 1:   log likelihood = -9701.5528  
Iteration 2:   log likelihood =  -9700.398  

Performing gradient-based optimization: 

Iteration 0:   log likelihood =  -9700.398  
Iteration 1:   log likelihood = -9699.8392  
Iteration 2:   log likelihood = -9699.8377  
Iteration 3:   log likelihood = -9699.8377  

Mixed-effects logistic regression               Number of obs     =     21,697
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         33
                                                              avg =      442.8
                                                              max =      1,985

Integration points =   7                        Wald chi2(19)     =    3092.08
Log likelihood = -9699.8377                     Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------------------
                     vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
              st_ideo7_dist2021 |   .3033319   .2708079     1.12   0.263    -.2274419    .8341057
affective_polarization_12_16_20 |   .2111599   1.240171     0.17   0.865     -2.21953     2.64185
     st_dist_affective_12_16_20 |  -.7587648   .6038795    -1.26   0.209    -1.942347    .4248172
                          ideo7 |  -.4751745   .0114393   -41.54   0.000    -.4975952   -.4527538
                   cz_imm_share |   1.168565   .2154822     5.42   0.000     .7462281    1.590903
                         female |  -.1824177   .0368602    -4.95   0.000    -.2546622   -.1101731
                            age |   .0352762   .0011643    30.30   0.000     .0329941    .0375583
                covid_diagnosed |   .4889628   .0395852    12.35   0.000     .4113774    .5665483
                             hs |    .634764   .0872502     7.28   0.000     .4637569    .8057712
                        college |   1.281453   .0937498    13.67   0.000     1.097707    1.465199
                           city |   .4594239   .0540235     8.50   0.000     .3535399    .5653079
                         suburb |   .4406344   .0497245     8.86   0.000     .3431761    .5380927
                           town |   .1928057   .0606655     3.18   0.001     .0739036    .3117079
                          white |   .5465531   .0750614     7.28   0.000     .3994354    .6936707
                          black |   .1875663   .0883323     2.12   0.034     .0144382    .3606944
                  race_hispanic |   .5439033   .0894812     6.08   0.000     .3685235    .7192832
                          asian |   1.369038    .161343     8.49   0.000     1.052812    1.685264
                    income_100k |   .5193598   .0559118     9.29   0.000     .4097747    .6289448
                     income_50k |   .3626064   .0416615     8.70   0.000     .2809513    .4442615
                          _cons |  -.7881703   .5695831    -1.38   0.166    -1.904533    .3281921
-------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .1391851   .0288044      .0927761    .2088091
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 20.50       Prob >= chibar2 = 0.0000
(est2 stored)

. eststo: xtmelogit vaccinated st_ideo7_dist2021 affective_polarization_12_16_20 st_dist_affective_12_16_
> 20 st_ideo7_mean2021  ideo7 cz_imm_share female age covid_diagnosed hs college  city suburb town white 
> black race_hispanic asian income_100k income_50k || inputstate: 

Refining starting values: 

Iteration 0:   log likelihood = -9753.7423  (not concave)
Iteration 1:   log likelihood = -9698.5766  
Iteration 2:   log likelihood = -9697.8005  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -9697.8005  
Iteration 1:   log likelihood = -9696.9698  
Iteration 2:   log likelihood =  -9696.967  
Iteration 3:   log likelihood =  -9696.967  

Mixed-effects logistic regression               Number of obs     =     21,697
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         33
                                                              avg =      442.8
                                                              max =      1,985

Integration points =   7                        Wald chi2(20)     =    3090.43
Log likelihood =  -9696.967                     Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------------------
                     vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
              st_ideo7_dist2021 |   .2650414   .2696024     0.98   0.326    -.2633696    .7934523
affective_polarization_12_16_20 |  -.0614146   1.208453    -0.05   0.959    -2.429938    2.307109
     st_dist_affective_12_16_20 |  -.6769439   .6010769    -1.13   0.260    -1.855033    .5011452
              st_ideo7_mean2021 |  -.2354852   .0954448    -2.47   0.014    -.4225537   -.0484167
                          ideo7 |  -.4729143   .0114589   -41.27   0.000    -.4953733   -.4504553
                   cz_imm_share |   .9920418   .2260278     4.39   0.000     .5490355    1.435048
                         female |  -.1812741   .0368636    -4.92   0.000    -.2535254   -.1090228
                            age |   .0351878   .0011649    30.21   0.000     .0329046    .0374709
                covid_diagnosed |   .4909032   .0395906    12.40   0.000      .413307    .5684995
                             hs |   .6343192   .0872263     7.27   0.000     .4633587    .8052797
                        college |   1.281723   .0937295    13.67   0.000     1.098017     1.46543
                           city |   .4590086   .0540014     8.50   0.000     .3531678    .5648494
                         suburb |   .4395843   .0496973     8.85   0.000     .3421794    .5369893
                           town |   .1905606   .0606647     3.14   0.002     .0716599    .3094612
                          white |   .5488076   .0750423     7.31   0.000     .4017275    .6958877
                          black |   .1982089   .0884343     2.24   0.025     .0248809    .3715369
                  race_hispanic |   .5455371   .0894327     6.10   0.000     .3702522    .7208219
                          asian |    1.36494   .1613381     8.46   0.000     1.048723    1.681157
                    income_100k |   .5180214   .0559141     9.26   0.000     .4084317    .6276111
                     income_50k |   .3618584   .0416644     8.69   0.000     .2801976    .4435192
                          _cons |   .2769802   .6989795     0.40   0.692    -1.092994    1.646955
-------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .1223855   .0287453      .0772332    .1939348
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 12.12       Prob >= chibar2 = 0.0003
(est3 stored)

. 
. eststo: xtmelogit vaccinated cz_ideo7_dist2021 affective_polarization_12_16_20 cz_dist_affective_12_16_
> 20 ideo7 cz_imm_share female age covid_diagnosed hs college  city suburb town white black race_hispanic
>  asian || czone:

Refining starting values: 

Iteration 0:   log likelihood = -10887.656  (not concave)
Iteration 1:   log likelihood = -10783.615  
Iteration 2:   log likelihood = -10748.875  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -10748.875  
Iteration 1:   log likelihood = -10744.419  
Iteration 2:   log likelihood = -10744.364  
Iteration 3:   log likelihood = -10744.364  

Mixed-effects logistic regression               Number of obs     =     23,908
Group variable: czone                           Number of groups  =        626

                                                Obs per group:
                                                              min =          1
                                                              avg =       38.2
                                                              max =      1,028

Integration points =   7                        Wald chi2(17)     =    3305.31
Log likelihood = -10744.364                     Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------------------
                     vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
              cz_ideo7_dist2021 |   .5361335   .2640184     2.03   0.042      .018667      1.0536
affective_polarization_12_16_20 |    1.19609   1.089773     1.10   0.272    -.9398256    3.332006
     cz_dist_affective_12_16_20 |  -1.287941   .5891428    -2.19   0.029     -2.44264   -.1332428
                          ideo7 |  -.4754945   .0108652   -43.76   0.000    -.4967899   -.4541991
                   cz_imm_share |   1.331119   .2450648     5.43   0.000     .8508007    1.811437
                         female |  -.2031977   .0349585    -5.81   0.000    -.2717151   -.1346802
                            age |   .0346819   .0010973    31.61   0.000     .0325313    .0368325
                covid_diagnosed |   .5292095   .0376021    14.07   0.000     .4555108    .6029083
                             hs |   .6904872   .0837578     8.24   0.000      .526325    .8546495
                        college |    1.44179   .0882213    16.34   0.000     1.268879      1.6147
                           city |   .4254779   .0522268     8.15   0.000     .3231152    .5278406
                         suburb |   .4601505   .0484406     9.50   0.000     .3652086    .5550924
                           town |   .1829681   .0578231     3.16   0.002     .0696369    .2962993
                          white |   .5836826   .0693309     8.42   0.000     .4477965    .7195686
                          black |   .1703657   .0827284     2.06   0.039     .0082211    .3325103
                  race_hispanic |   .5346899    .083535     6.40   0.000     .3709642    .6984155
                          asian |   1.321946   .1477283     8.95   0.000     1.032404    1.611488
                          _cons |  -1.161637   .5023822    -2.31   0.021    -2.146288    -.176986
-------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .2203727   .0301032      .1686097    .2880269
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 41.19       Prob >= chibar2 = 0.0000
(est4 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 affective_polarization_12_16_20 cz_dist_affective_12_16_
> 20 ideo7 cz_imm_share female age covid_diagnosed hs college  city suburb town white black race_hispanic
>  asian income_100k income_50k || czone:

Refining starting values: 

Iteration 0:   log likelihood = -9840.1173  (not concave)
Iteration 1:   log likelihood = -9744.4693  
Iteration 2:   log likelihood = -9736.3864  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -9736.3864  
Iteration 1:   log likelihood = -9710.1855  
Iteration 2:   log likelihood = -9697.1059  
Iteration 3:   log likelihood = -9695.6734  
Iteration 4:   log likelihood = -9695.6677  
Iteration 5:   log likelihood = -9695.6677  

Mixed-effects logistic regression               Number of obs     =     21,697
Group variable: czone                           Number of groups  =        622

                                                Obs per group:
                                                              min =          1
                                                              avg =       34.9
                                                              max =        938

Integration points =   7                        Wald chi2(19)     =    3044.33
Log likelihood = -9695.6677                     Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------------------
                     vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
              cz_ideo7_dist2021 |   .4369988   .2749209     1.59   0.112    -.1018363     .975834
affective_polarization_12_16_20 |   .8987014   1.122803     0.80   0.423    -1.301952    3.099355
     cz_dist_affective_12_16_20 |  -1.055129   .6133956    -1.72   0.085    -2.257362    .1471041
                          ideo7 |  -.4769439   .0114088   -41.80   0.000    -.4993048    -.454583
                   cz_imm_share |   1.147929   .2414367     4.75   0.000     .6747213    1.621136
                         female |  -.1818918   .0369869    -4.92   0.000    -.2543847   -.1093989
                            age |   .0352547   .0011671    30.21   0.000     .0329673    .0375421
                covid_diagnosed |   .4904278   .0397557    12.34   0.000     .4125082    .5683475
                             hs |    .639812   .0875851     7.31   0.000     .4681485    .8114756
                        college |   1.286536   .0941176    13.67   0.000     1.102069    1.471003
                           city |   .4485068   .0545784     8.22   0.000     .3415351    .5554786
                         suburb |   .4269019   .0506964     8.42   0.000     .3275388    .5262649
                           town |   .1908578   .0609388     3.13   0.002       .07142    .3102957
                          white |   .5540713    .075328     7.36   0.000     .4064311    .7017116
                          black |   .1952673   .0885766     2.20   0.027     .0216604    .3688742
                  race_hispanic |   .5388105   .0897437     6.00   0.000     .3629161    .7147049
                          asian |   1.376738   .1617986     8.51   0.000     1.059619    1.693857
                    income_100k |    .509315   .0562044     9.06   0.000     .3991565    .6194736
                     income_50k |    .357315   .0418056     8.55   0.000     .2753775    .4392525
                          _cons |  -1.111913   .5189924    -2.14   0.032    -2.129119   -.0947061
-------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .1965561   .0306061      .1448586    .2667035
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 28.10       Prob >= chibar2 = 0.0000
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 affective_polarization_12_16_20 cz_dist_affective_12_16_
> 20 cz_ideo7_mean2021 ideo7 cz_imm_share female age covid_diagnosed hs college  city suburb town white b
> lack race_hispanic asian income_100k income_50k || czone:

Refining starting values: 

Iteration 0:   log likelihood = -9840.6553  (not concave)
Iteration 1:   log likelihood = -9744.0889  
Iteration 2:   log likelihood = -9690.7549  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -9690.7549  
Iteration 1:   log likelihood = -9687.9876  
Iteration 2:   log likelihood = -9687.9438  
Iteration 3:   log likelihood = -9687.9438  

Mixed-effects logistic regression               Number of obs     =     21,697
Group variable: czone                           Number of groups  =        622

                                                Obs per group:
                                                              min =          1
                                                              avg =       34.9
                                                              max =        938

Integration points =   7                        Wald chi2(20)     =    3080.15
Log likelihood = -9687.9438                     Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------------------
                     vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
              cz_ideo7_dist2021 |   .3646038   .2737005     1.33   0.183    -.1718393    .9010468
affective_polarization_12_16_20 |   .6219175   1.108801     0.56   0.575    -1.551293    2.795128
     cz_dist_affective_12_16_20 |  -.9121146   .6103396    -1.49   0.135    -2.108358     .284129
              cz_ideo7_mean2021 |  -.1780004   .0444445    -4.01   0.000    -.2651101   -.0908907
                          ideo7 |  -.4678742   .0115676   -40.45   0.000    -.4905462   -.4452021
                   cz_imm_share |    .806626   .2393174     3.37   0.001     .3375726    1.275679
                         female |  -.1814467   .0369613    -4.91   0.000    -.2538895   -.1090039
                            age |   .0351935   .0011661    30.18   0.000      .032908    .0374791
                covid_diagnosed |   .4927677   .0397102    12.41   0.000     .4149371    .5705983
                             hs |   .6411472   .0875121     7.33   0.000     .4696266    .8126678
                        college |   1.287371   .0940415    13.69   0.000     1.103053    1.471689
                           city |   .4284125   .0546529     7.84   0.000     .3212947    .5355302
                         suburb |   .4045135   .0507788     7.97   0.000      .304989     .504038
                           town |   .1838535    .060938     3.02   0.003     .0644173    .3032898
                          white |   .5553301   .0752551     7.38   0.000     .4078328    .7028274
                          black |   .1968357   .0883753     2.23   0.026     .0236232    .3700481
                  race_hispanic |   .5425682    .089582     6.06   0.000     .3669907    .7181457
                          asian |   1.371148   .1615256     8.49   0.000     1.054564    1.687733
                    income_100k |     .50471   .0561372     8.99   0.000     .3946831    .6147368
                     income_50k |   .3559705   .0417799     8.52   0.000     .2740835    .4378575
                          _cons |  -.2413171   .5544284    -0.44   0.663    -1.327977    .8453426
-------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .1616593   .0316507      .1101405    .2372765
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 15.44       Prob >= chibar2 = 0.0000
(est6 stored)

. 
. esttab using "TableA9.tex", label keep(st_ideo7_dist2021 cz_ideo7_dist2021 affective_polarization_12_16
> _20 st_dist_affective_12_16_20 cz_dist_affective_12_16_20 cz_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_
> mean2021 ideo7 cz_imm_share female age covid_diagnosed hs college income_50k income_100k city suburb to
> wn white black race_hispanic asian covid_diagnosed) order(st_ideo7_dist2021 cz_ideo7_dist2021 affective
> _polarization_12_16_20 st_dist_affective_12_16_20 cz_dist_affective_12_16_20 cz_ideo7_dist2021 st_ideo7
> _mean2021  cz_ideo7_mean2021  ideo7 cz_imm_share female age covid_diagnosed hs college income_50k incom
> e_100k city suburb town white black race_hispanic asian covid_diagnosed) margin nonotes se(3) b(3) repl
> ace star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars 
(output written to TableA9.tex)

. 
. * Table A10: Results: Interactive Effect of City and Ideological Distance
. 
. eststo clear 

. 
. eststo: regress vaccinated st_ideo7_dist2021 city city_st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_imm
> _share_nonwhite female age covid_diagnosed hs college white black race_hispanic asian , cluster(inputst
> ate)

Linear regression                               Number of obs     =     23,908
                                                F(15, 48)         =     216.77
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1786
                                                Root MSE          =     .38637

                                      (Std. Err. adjusted for 49 clusters in inputstate)
----------------------------------------------------------------------------------------
                       |               Robust
            vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
     st_ideo7_dist2021 |  -.0244349    .002937    -8.32   0.000    -.0303402   -.0185295
                  city |   .0176751    .012472     1.42   0.163    -.0074017    .0427518
city_st_ideo7_dist2021 |  -.0005212   .0060037    -0.09   0.931    -.0125925      .01155
                 ideo7 |  -.0705133   .0021566   -32.70   0.000    -.0748494   -.0661771
     st_ideo7_mean2021 |  -.0371208   .0129798    -2.86   0.006    -.0632184   -.0110231
 cz_imm_share_nonwhite |   .2992552    .079102     3.78   0.000     .1402101    .4583004
                female |  -.0358553   .0053129    -6.75   0.000    -.0465376   -.0251731
                   age |   .0050023   .0002483    20.15   0.000     .0045032    .0055015
       covid_diagnosed |    .082207   .0073628    11.17   0.000     .0674032    .0970109
                    hs |   .1450969   .0184189     7.88   0.000     .1080633    .1821305
               college |   .2506056   .0174759    14.34   0.000      .215468    .2857432
                 white |   .0951818   .0150195     6.34   0.000     .0649831    .1253806
                 black |   .0479111   .0150333     3.19   0.003     .0176846    .0781375
         race_hispanic |   .0956442   .0164526     5.81   0.000     .0625641    .1287243
                 asian |   .1804129   .0208886     8.64   0.000     .1384136    .2224121
                 _cons |   .6276134   .0576666    10.88   0.000     .5116669    .7435598
----------------------------------------------------------------------------------------
(est1 stored)

. eststo: areg  vaccinated st_ideo7_dist2021 city city_st_ideo7_dist2021 ideo7 cz_imm_share_nonwhite fema
> le age covid_diagnosed hs college  white black race_hispanic asian , cluster(inputstate) absorb(inputst
> ate)

Linear regression, absorbing indicators         Number of obs     =     23,908
Absorbed variable: inputstate                   No. of categories =         49
                                                F(  14,     48)   =     205.21
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1820
                                                Adj R-squared     =     0.1799
                                                Root MSE          =     0.3859

                                      (Std. Err. adjusted for 49 clusters in inputstate)
----------------------------------------------------------------------------------------
                       |               Robust
            vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
     st_ideo7_dist2021 |  -.0242095   .0029282    -8.27   0.000    -.0300971   -.0183219
                  city |   .0200838   .0130329     1.54   0.130    -.0061206    .0462882
city_st_ideo7_dist2021 |  -.0004086   .0060869    -0.07   0.947    -.0126472    .0118299
                 ideo7 |   -.070471   .0021647   -32.56   0.000    -.0748234   -.0661187
 cz_imm_share_nonwhite |   .4032567   .0957468     4.21   0.000     .2107449    .5957685
                female |  -.0356598   .0053826    -6.63   0.000    -.0464821   -.0248375
                   age |   .0050571   .0002483    20.36   0.000     .0045579    .0055564
       covid_diagnosed |   .0820068   .0073628    11.14   0.000     .0672029    .0968106
                    hs |   .1441647   .0183393     7.86   0.000     .1072911    .1810383
               college |   .2469406   .0176053    14.03   0.000     .2115428    .2823384
                 white |   .0918618   .0150675     6.10   0.000     .0615665     .122157
                 black |   .0393366   .0152232     2.58   0.013     .0087282    .0699449
         race_hispanic |   .0962232   .0170863     5.63   0.000      .061869    .1305775
                 asian |   .1825987   .0209053     8.73   0.000     .1405658    .2246315
                 _cons |   .4747657   .0376171    12.62   0.000     .3991314       .5504
----------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: regress vaccinated cz_ideo7_dist2021 city city_cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_imm
> _share_nonwhite female age covid_diagnosed hs college  white black race_hispanic asian, cluster(czone)

Linear regression                               Number of obs     =     23,908
                                                F(15, 625)        =     143.09
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1792
                                                Root MSE          =     .38622

                                          (Std. Err. adjusted for 626 clusters in czone)
----------------------------------------------------------------------------------------
                       |               Robust
            vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
     cz_ideo7_dist2021 |  -.0218595   .0031247    -7.00   0.000    -.0279957   -.0157233
                  city |   .0174542   .0108896     1.60   0.109    -.0039304    .0388388
city_cz_ideo7_dist2021 |  -.0012149   .0056098    -0.22   0.829    -.0122312    .0098013
                 ideo7 |  -.0687653   .0022554   -30.49   0.000    -.0731944   -.0643362
     cz_ideo7_mean2021 |  -.0467212   .0073365    -6.37   0.000    -.0611284   -.0323141
 cz_imm_share_nonwhite |   .2306884   .0604679     3.82   0.000     .1119434    .3494334
                female |  -.0353324   .0051997    -6.80   0.000    -.0455435   -.0251213
                   age |   .0049819   .0002175    22.91   0.000     .0045548     .005409
       covid_diagnosed |   .0818723   .0064814    12.63   0.000     .0691443    .0946003
                    hs |   .1448304   .0178507     8.11   0.000     .1097758     .179885
               college |   .2475249   .0174785    14.16   0.000     .2132013    .2818486
                 white |   .0946605   .0137249     6.90   0.000      .067708     .121613
                 black |   .0460757   .0148561     3.10   0.002     .0169018    .0752496
         race_hispanic |   .0969887   .0158828     6.11   0.000     .0657986    .1281788
                 asian |   .1821292   .0188798     9.65   0.000     .1450537    .2192047
                 _cons |   .6625854    .041949    15.80   0.000     .5802072    .7449635
----------------------------------------------------------------------------------------
(est3 stored)

. eststo: areg vaccinated cz_ideo7_dist2021 city city_cz_ideo7_dist2021 ideo7 female age covid_diagnosed 
> hs college  white black race_hispanic asian , cluster(czone) absorb(czone)

Linear regression, absorbing indicators         Number of obs     =     23,908
Absorbed variable: czone                        No. of categories =        626
                                                F(  13,    625)   =     117.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2120
                                                Adj R-squared     =     0.1904
                                                Root MSE          =     0.3835

                                          (Std. Err. adjusted for 626 clusters in czone)
----------------------------------------------------------------------------------------
                       |               Robust
            vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
     cz_ideo7_dist2021 |  -.0221565   .0032151    -6.89   0.000    -.0284702   -.0158428
                  city |   .0149232   .0109924     1.36   0.175    -.0066634    .0365098
city_cz_ideo7_dist2021 |   -.000676   .0057051    -0.12   0.906    -.0118794    .0105274
                 ideo7 |  -.0690539   .0023109   -29.88   0.000    -.0735919   -.0645158
                female |  -.0333895   .0052852    -6.32   0.000    -.0437684   -.0230107
                   age |    .004953   .0002213    22.38   0.000     .0045185    .0053875
       covid_diagnosed |    .082355   .0067286    12.24   0.000     .0691416    .0955684
                    hs |   .1405753   .0184172     7.63   0.000     .1044083    .1767423
               college |   .2376021   .0180756    13.14   0.000     .2021059    .2730983
                 white |   .0899854   .0141498     6.36   0.000     .0621986    .1177723
                 black |   .0327145   .0147757     2.21   0.027     .0036984    .0617306
         race_hispanic |   .0928709   .0159231     5.83   0.000     .0616017    .1241402
                 asian |   .1797532   .0191438     9.39   0.000     .1421593    .2173471
                 _cons |   .5139096   .0269257    19.09   0.000     .4610338    .5667855
----------------------------------------------------------------------------------------
(est4 stored)

. 
. eststo: xtmelogit vaccinated st_ideo7_dist2021 city city_st_ideo7_dist2021 ideo7 st_ideo7_mean2021 cz_i
> mm_share_nonwhite female age covid_diagnosed hs college white black race_hispanic asian || inputstate: 

Refining starting values: 

Iteration 0:   log likelihood = -10857.055  (not concave)
Iteration 1:   log likelihood = -10805.214  
Iteration 2:   log likelihood = -10801.504  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -10801.504  
Iteration 1:   log likelihood = -10800.886  
Iteration 2:   log likelihood = -10800.883  
Iteration 3:   log likelihood = -10800.883  

Mixed-effects logistic regression               Number of obs     =     23,908
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         34
                                                              avg =      487.9
                                                              max =      2,187

Integration points =   7                        Wald chi2(15)     =    3319.89
Log likelihood = -10800.883                     Prob > chi2       =     0.0000

----------------------------------------------------------------------------------------
            vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
     st_ideo7_dist2021 |  -.0424867   .0193359    -2.20   0.028    -.0803844    -.004589
                  city |   .1464862   .0654498     2.24   0.025     .0182069    .2747654
city_st_ideo7_dist2021 |  -.0028255   .0354206    -0.08   0.936    -.0722485    .0665975
                 ideo7 |  -.4736307   .0109353   -43.31   0.000    -.4950635   -.4521979
     st_ideo7_mean2021 |  -.2174847   .0940385    -2.31   0.021    -.4017967   -.0331727
 cz_imm_share_nonwhite |   2.841338   .3451469     8.23   0.000     2.164863    3.517814
                female |  -.2174652   .0346758    -6.27   0.000    -.2854286   -.1495019
                   age |   .0341064   .0010873    31.37   0.000     .0319754    .0362374
       covid_diagnosed |   .5204092   .0372761    13.96   0.000     .4473494     .593469
                    hs |   .7228318   .0830424     8.70   0.000     .5600717     .885592
               college |   1.507387    .087361    17.25   0.000     1.336162    1.678611
                 white |   .5936671   .0688712     8.62   0.000      .458682    .7286522
                 black |   .2081649   .0821082     2.54   0.011     .0472358     .369094
         race_hispanic |   .5785533   .0828229     6.99   0.000     .4162233    .7408833
                 asian |   1.327458   .1471715     9.02   0.000     1.039007    1.615909
                 _cons |   .4759441   .3973195     1.20   0.231    -.3027878    1.254676
----------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .1298705   .0280478      .0850509    .1983089
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 17.94       Prob >= chibar2 = 0.0000
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 city city_cz_ideo7_dist2021 ideo7 cz_ideo7_mean2021 cz_i
> mm_share_nonwhite female age covid_diagnosed hs college white black race_hispanic asian || czone: 

Refining starting values: 

Iteration 0:   log likelihood = -10924.613  (not concave)
Iteration 1:   log likelihood = -10820.283  
Iteration 2:   log likelihood = -10785.302  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -10785.302  
Iteration 1:   log likelihood = -10779.901  
Iteration 2:   log likelihood = -10779.643  
Iteration 3:   log likelihood = -10779.643  

Mixed-effects logistic regression               Number of obs     =     23,908
Group variable: czone                           Number of groups  =        626

                                                Obs per group:
                                                              min =          1
                                                              avg =       38.2
                                                              max =      1,028

Integration points =   7                        Wald chi2(15)     =    3282.13
Log likelihood = -10779.643                     Prob > chi2       =     0.0000

----------------------------------------------------------------------------------------
            vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
     cz_ideo7_dist2021 |  -.0479332   .0198696    -2.41   0.016    -.0868768   -.0089895
                  city |   .1333984   .0669997     1.99   0.046     .0020815    .2647153
city_cz_ideo7_dist2021 |  -.0011044   .0364787    -0.03   0.976    -.0726012    .0703925
                 ideo7 |  -.4676336   .0110207   -42.43   0.000    -.4892338   -.4460334
     cz_ideo7_mean2021 |  -.2077398   .0451015    -4.61   0.000     -.296137   -.1193426
 cz_imm_share_nonwhite |   2.354475    .422637     5.57   0.000     1.526121    3.182828
                female |   -.215024   .0348256    -6.17   0.000    -.2832809   -.1467671
                   age |   .0341756   .0010913    31.32   0.000     .0320367    .0363146
       covid_diagnosed |   .5257899   .0374592    14.04   0.000     .4523712    .5992085
                    hs |   .7221891   .0834585     8.65   0.000     .5586135    .8857647
               college |    1.49762   .0878095    17.06   0.000     1.325516    1.669723
                 white |   .5969818   .0691818     8.63   0.000     .4613879    .7325757
                 black |   .2012972   .0823503     2.44   0.015     .0398935    .3627009
         race_hispanic |     .57388   .0830867     6.91   0.000     .4110331    .7367269
                 asian |   1.326562   .1474351     9.00   0.000     1.037594     1.61553
                 _cons |   .4336014   .2279476     1.90   0.057    -.0131678    .8803706
----------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |    .195472   .0314452      .1426112    .2679265
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 29.31       Prob >= chibar2 = 0.0000
(est6 stored)

. 
. esttab using "TableA10.tex", label keep(st_ideo7_dist2021 city city_st_ideo7_dist2021 st_ideo7_mean2021
>  cz_ideo7_dist2021 city_cz_ideo7_dist2021 cz_ideo7_mean2021 ideo7 cz_imm_share_nonwhite female age covi
> d_diagnosed hs college white black race_hispanic asian covid_diagnosed) order(st_ideo7_dist2021 city_st
> _ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 city_cz_ideo7_dist2021 cz_ideo7_mean2021 ideo7 city
>  cz_imm_share_nonwhite female age covid_diagnosed hs college white black race_hispanic asian covid_diag
> nosed) margin nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars 
(output written to TableA10.tex)

. 
. * Table A11: Results: Interactive Effect of Political Interest and Ideological Distance
. 
. eststo clear

. 
. eststo: regress vaccinated st_ideo7_dist2021 high_pol_interest pol_interest_st_ideo7_dist2021 ideo7 st_
> ideo7_mean2021 cz_imm_share female age covid_diagnosed hs college income_50k income_100k city suburb to
> wn white black race_hispanic asian , cluster(inputstate)

Linear regression                               Number of obs     =     21,697
                                                F(20, 48)         =     121.65
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1860
                                                Root MSE          =      .3842

                                              (Std. Err. adjusted for 49 clusters in inputstate)
------------------------------------------------------------------------------------------------
                               |               Robust
                    vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
             st_ideo7_dist2021 |  -.0127681   .0041923    -3.05   0.004    -.0211972   -.0043389
             high_pol_interest |   .0387171   .0095906     4.04   0.000     .0194339    .0580004
pol_interest_st_ideo7_dist2021 |   -.021052   .0053876    -3.91   0.000    -.0318845   -.0102196
                         ideo7 |  -.0691472    .002162   -31.98   0.000    -.0734943   -.0648002
             st_ideo7_mean2021 |  -.0471205     .01237    -3.81   0.000    -.0719921    -.022249
                  cz_imm_share |   .0806622   .0351851     2.29   0.026     .0099179    .1514066
                        female |   -.029604   .0062331    -4.75   0.000    -.0421365   -.0170715
                           age |   .0050491   .0002657    19.00   0.000     .0045148    .0055833
               covid_diagnosed |   .0763543   .0084193     9.07   0.000     .0594261    .0932825
                            hs |   .1328134   .0195018     6.81   0.000     .0936024    .1720244
                       college |   .2155075    .018443    11.69   0.000     .1784253    .2525897
                    income_50k |   .0541868   .0072734     7.45   0.000     .0395626    .0688111
                   income_100k |   .0660707   .0075424     8.76   0.000     .0509057    .0812357
                          city |   .0769568   .0090133     8.54   0.000     .0588344    .0950792
                        suburb |   .0779547   .0077485    10.06   0.000     .0623753    .0935341
                          town |   .0389491   .0100842     3.86   0.000     .0186735    .0592247
                         white |   .0855701   .0143651     5.96   0.000     .0566872     .114453
                         black |   .0408438   .0149393     2.73   0.009     .0108062    .0708813
                 race_hispanic |   .0865489    .016348     5.29   0.000      .053679    .1194188
                         asian |   .1756013   .0213732     8.22   0.000     .1326276    .2185749
                         _cons |   .5993778    .054942    10.91   0.000     .4889094    .7098462
------------------------------------------------------------------------------------------------
(est1 stored)

. eststo: areg  vaccinated st_ideo7_dist2021 high_pol_interest pol_interest_st_ideo7_dist2021  ideo7 cz_i
> mm_share female age covid_diagnosed hs college income_50k income_100k city suburb town white black race
> _hispanic asian , cluster(inputstate) absorb(inputstate)

Linear regression, absorbing indicators         Number of obs     =     21,697
Absorbed variable: inputstate                   No. of categories =         49
                                                F(  19,     48)   =     101.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1891
                                                Adj R-squared     =     0.1866
                                                Root MSE          =     0.3839

                                              (Std. Err. adjusted for 49 clusters in inputstate)
------------------------------------------------------------------------------------------------
                               |               Robust
                    vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
             st_ideo7_dist2021 |  -.0128908   .0041916    -3.08   0.003    -.0213186    -.004463
             high_pol_interest |   .0380571   .0097118     3.92   0.000     .0185303     .057584
pol_interest_st_ideo7_dist2021 |  -.0203148   .0054176    -3.75   0.000    -.0312077   -.0094219
                         ideo7 |   -.069153    .002165   -31.94   0.000     -.073506      -.0648
                  cz_imm_share |   .1462594   .0405627     3.61   0.001     .0647026    .2278162
                        female |  -.0294259    .006274    -4.69   0.000    -.0420405   -.0168112
                           age |   .0051013   .0002679    19.04   0.000     .0045625      .00564
               covid_diagnosed |   .0766172   .0083823     9.14   0.000     .0597634     .093471
                            hs |   .1326319   .0192755     6.88   0.000     .0938759     .171388
                       college |   .2134349   .0184509    11.57   0.000     .1763369    .2505329
                    income_50k |   .0542842   .0073631     7.37   0.000     .0394798    .0690886
                   income_100k |   .0642295   .0075249     8.54   0.000     .0490997    .0793592
                          city |   .0793829   .0094963     8.36   0.000     .0602893    .0984764
                        suburb |   .0783474   .0075823    10.33   0.000     .0631022    .0935927
                          town |   .0375939   .0101022     3.72   0.001     .0172821    .0579058
                         white |   .0838085   .0145724     5.75   0.000     .0545087    .1131084
                         black |   .0337337   .0149262     2.26   0.028     .0037226    .0637449
                 race_hispanic |   .0895149   .0175806     5.09   0.000     .0541668    .1248629
                         asian |   .1762013   .0214296     8.22   0.000     .1331143    .2192883
                         _cons |   .4043293   .0357416    11.31   0.000      .332466    .4761927
------------------------------------------------------------------------------------------------
(est2 stored)

. 
. eststo: regress vaccinated cz_ideo7_dist2021 high_pol_interest pol_interest_cz_ideo7_dist2021  ideo7 cz
> _ideo7_mean2021 cz_imm_share female age covid_diagnosed hs college income_50k income_100k city suburb t
> own white black race_hispanic asian , cluster(czone)

Linear regression                               Number of obs     =     21,697
                                                F(20, 621)        =     101.72
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1859
                                                Root MSE          =     .38421

                                                  (Std. Err. adjusted for 622 clusters in czone)
------------------------------------------------------------------------------------------------
                               |               Robust
                    vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
             cz_ideo7_dist2021 |  -.0117218   .0041163    -2.85   0.005    -.0198053   -.0036382
             high_pol_interest |   .0323368   .0101687     3.18   0.002     .0123676    .0523061
pol_interest_cz_ideo7_dist2021 |  -.0187877    .005471    -3.43   0.001    -.0295317   -.0080438
                         ideo7 |  -.0678073   .0022066   -30.73   0.000    -.0721405   -.0634741
             cz_ideo7_mean2021 |  -.0402818   .0072527    -5.55   0.000    -.0545245   -.0260391
                  cz_imm_share |   .0709982   .0338047     2.10   0.036     .0046129    .1373836
                        female |  -.0298445   .0054665    -5.46   0.000    -.0405795   -.0191094
                           age |   .0050522   .0002239    22.56   0.000     .0046125    .0054919
               covid_diagnosed |   .0761211   .0071412    10.66   0.000     .0620972    .0901449
                            hs |   .1333666   .0188606     7.07   0.000     .0963284    .1704048
                       college |   .2144437   .0186512    11.50   0.000     .1778167    .2510706
                    income_50k |   .0537285   .0068146     7.88   0.000     .0403461     .067111
                   income_100k |   .0651859   .0078391     8.32   0.000     .0497915    .0805803
                          city |   .0722505   .0097367     7.42   0.000     .0531297    .0913714
                        suburb |   .0729817   .0086602     8.43   0.000     .0559749    .0899885
                          town |   .0378143   .0106244     3.56   0.000     .0169503    .0586784
                         white |   .0855426   .0141258     6.06   0.000     .0578025    .1132827
                         black |   .0388739   .0151517     2.57   0.011      .009119    .0686287
                 race_hispanic |   .0877701    .016063     5.46   0.000     .0562257    .1193144
                         asian |   .1770594   .0197315     8.97   0.000     .1383109    .2158079
                         _cons |   .5717873   .0409001    13.98   0.000     .4914681    .6521065
------------------------------------------------------------------------------------------------
(est3 stored)

. eststo: areg vaccinated cz_ideo7_dist2021 high_pol_interest pol_interest_cz_ideo7_dist2021 ideo7 female
>  age covid_diagnosed hs college income_50k income_100k city suburb town white black race_hispanic asian
> , cluster(czone) absorb(czone)

Linear regression, absorbing indicators         Number of obs     =     21,697
Absorbed variable: czone                        No. of categories =        622
                                                F(  18,    621)   =      76.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2187
                                                Adj R-squared     =     0.1950
                                                Root MSE          =     0.3819

                                                  (Std. Err. adjusted for 622 clusters in czone)
------------------------------------------------------------------------------------------------
                               |               Robust
                    vaccinated |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
             cz_ideo7_dist2021 |  -.0103081   .0041698    -2.47   0.014    -.0184966   -.0021195
             high_pol_interest |   .0339565   .0104071     3.26   0.001     .0135191    .0543938
pol_interest_cz_ideo7_dist2021 |  -.0208615     .00558    -3.74   0.000    -.0318196   -.0099035
                         ideo7 |  -.0679843   .0022402   -30.35   0.000    -.0723836   -.0635851
                        female |  -.0288872   .0055909    -5.17   0.000    -.0398665   -.0179079
                           age |     .00503   .0002323    21.66   0.000     .0045739    .0054862
               covid_diagnosed |   .0774329   .0073972    10.47   0.000     .0629063    .0919595
                            hs |   .1322475   .0193133     6.85   0.000     .0943201    .1701748
                       college |   .2110255   .0190643    11.07   0.000     .1735872    .2484638
                    income_50k |   .0514865   .0069615     7.40   0.000     .0378156    .0651574
                   income_100k |   .0599728   .0080772     7.42   0.000      .044111    .0758347
                          city |   .0676893   .0110168     6.14   0.000     .0460546    .0893241
                        suburb |   .0659149   .0101241     6.51   0.000     .0460333    .0857964
                          town |   .0338191   .0113206     2.99   0.003     .0115878    .0560503
                         white |   .0838778   .0147713     5.68   0.000       .05487    .1128855
                         black |    .030279   .0152334     1.99   0.047     .0003637    .0601942
                 race_hispanic |   .0893704   .0166486     5.37   0.000      .056676    .1220649
                         asian |   .1762329    .020189     8.73   0.000      .136586    .2158798
                         _cons |   .4349715   .0288072    15.10   0.000     .3784001    .4915429
------------------------------------------------------------------------------------------------
(est4 stored)

. 
. eststo: xtmelogit vaccinated st_ideo7_dist2021 high_pol_interest pol_interest_st_ideo7_dist2021 ideo7 s
> t_ideo7_mean2021 cz_imm_share female age covid_diagnosed hs college income_50k income_100k city suburb 
> town white black race_hispanic asian || inputstate: 

Refining starting values: 

Iteration 0:   log likelihood = -9747.8207  (not concave)
Iteration 1:   log likelihood = -9692.9238  
Iteration 2:   log likelihood = -9692.0696  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -9692.0696  
Iteration 1:   log likelihood = -9691.2038  
Iteration 2:   log likelihood = -9691.2004  
Iteration 3:   log likelihood = -9691.2004  

Mixed-effects logistic regression               Number of obs     =     21,697
Group variable: inputstate                      Number of groups  =         49

                                                Obs per group:
                                                              min =         33
                                                              avg =      442.8
                                                              max =      1,985

Integration points =   7                        Wald chi2(20)     =    3110.04
Log likelihood = -9691.2004                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------------------------
                    vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
             st_ideo7_dist2021 |   .0053946   .0226401     0.24   0.812    -.0389792    .0497684
             high_pol_interest |   .2541991   .0687602     3.70   0.000     .1194316    .3889667
pol_interest_st_ideo7_dist2021 |  -.1174574   .0341947    -3.43   0.001    -.1844778    -.050437
                         ideo7 |  -.4681968   .0114653   -40.84   0.000    -.4906684   -.4457253
             st_ideo7_mean2021 |  -.2441885   .0958739    -2.55   0.011     -.432098    -.056279
                  cz_imm_share |   .9753216   .2269521     4.30   0.000     .5305036     1.42014
                        female |  -.1730081   .0373288    -4.63   0.000    -.2461712    -.099845
                           age |   .0345299   .0012054    28.65   0.000     .0321673    .0368924
               covid_diagnosed |   .4872073   .0396467    12.29   0.000     .4095012    .5649135
                            hs |   .6309411   .0872554     7.23   0.000     .4599236    .8019585
                       college |   1.271858    .093987    13.53   0.000     1.087647    1.456069
                    income_50k |   .3553101   .0417648     8.51   0.000     .2734525    .4371676
                   income_100k |   .5043122   .0563052     8.96   0.000      .393956    .6146684
                          city |   .4620359   .0540079     8.55   0.000     .3561824    .5678894
                        suburb |   .4392005   .0497184     8.83   0.000     .3417542    .5366468
                          town |   .1919776   .0606716     3.16   0.002     .0730634    .3108918
                         white |   .5516572   .0751043     7.35   0.000     .4044555    .6988588
                         black |   .1994952   .0884379     2.26   0.024     .0261601    .3728303
                 race_hispanic |    .548309   .0895534     6.12   0.000     .3727877    .7238304
                         asian |   1.365261   .1611339     8.47   0.000     1.049444    1.681077
                         _cons |   .2207682    .407822     0.54   0.588    -.5785482    1.020085
------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
inputstate: Identity         |
                   sd(_cons) |   .1238793   .0288715      .0784542    .1956057
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 12.54       Prob >= chibar2 = 0.0002
(est5 stored)

. eststo: xtmelogit vaccinated cz_ideo7_dist2021 high_pol_interest pol_interest_cz_ideo7_dist2021 ideo7 c
> z_ideo7_mean2021 cz_imm_share female age covid_diagnosed hs college income_50k income_100k city suburb 
> town white black race_hispanic asian || czone: 

Refining starting values: 

Iteration 0:   log likelihood = -9835.1051  (not concave)
Iteration 1:   log likelihood = -9738.5711  
Iteration 2:   log likelihood = -9685.7583  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -9685.7583  
Iteration 1:   log likelihood = -9683.1592  
Iteration 2:   log likelihood = -9683.1099  
Iteration 3:   log likelihood = -9683.1098  

Mixed-effects logistic regression               Number of obs     =     21,697
Group variable: czone                           Number of groups  =        622

                                                Obs per group:
                                                              min =          1
                                                              avg =       34.9
                                                              max =        938

Integration points =   7                        Wald chi2(20)     =    3090.55
Log likelihood = -9683.1098                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------------------------
                    vaccinated |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
             cz_ideo7_dist2021 |  -.0006068   .0234598    -0.03   0.979    -.0465871    .0453735
             high_pol_interest |   .2403001    .068793     3.49   0.000     .1054683    .3751319
pol_interest_cz_ideo7_dist2021 |  -.1131243   .0350671    -3.23   0.001    -.1818545   -.0443941
                         ideo7 |  -.4632139   .0115761   -40.01   0.000    -.4859026   -.4405251
             cz_ideo7_mean2021 |  -.1904539   .0447105    -4.26   0.000    -.2780848   -.1028229
                  cz_imm_share |    .785071   .2410513     3.26   0.001     .3126191    1.257523
                        female |   -.174094   .0374318    -4.65   0.000     -.247459   -.1007291
                           age |   .0345495   .0012077    28.61   0.000     .0321824    .0369166
               covid_diagnosed |   .4893394    .039772    12.30   0.000     .4113877     .567291
                            hs |   .6379075   .0875656     7.28   0.000      .466282    .8095329
                       college |   1.277024   .0943174    13.54   0.000     1.092165    1.461882
                    income_50k |   .3497344   .0418857     8.35   0.000     .2676399    .4318288
                   income_100k |   .4915559   .0565258     8.70   0.000     .3807673    .6023444
                          city |   .4316561   .0546762     7.89   0.000     .3244927    .5388195
                        suburb |   .4051218   .0508251     7.97   0.000     .3055065    .5047371
                          town |   .1851648   .0609349     3.04   0.002     .0657346    .3045951
                         white |   .5588993   .0753056     7.42   0.000      .411303    .7064956
                         black |   .1983062   .0883936     2.24   0.025      .025058    .3715544
                 race_hispanic |   .5460837   .0897062     6.09   0.000     .3702629    .7219046
                         asian |   1.371462   .1613174     8.50   0.000     1.055286    1.687639
                         _cons |   .0217181   .2316998     0.09   0.925    -.4324051    .4758413
------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
czone: Identity              |
                   sd(_cons) |   .1657713   .0310841      .1147886    .2393975
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 17.46       Prob >= chibar2 = 0.0000
(est6 stored)

. 
. esttab using "TableA11.tex", label keep(st_ideo7_dist2021 high_pol_interest pol_interest_st_ideo7_dist2
> 021 pol_interest_cz_ideo7_dist2021 st_ideo7_mean2021 cz_ideo7_dist2021 cz_ideo7_mean2021 ideo7 cz_imm_s
> hare female age covid_diagnosed hs college income_50k income_100k city suburb town white black race_his
> panic asian covid_diagnosed) order(st_ideo7_dist2021 high_pol_interest pol_interest_st_ideo7_dist2021 c
> z_ideo7_dist2021 pol_interest_cz_ideo7_dist2021 st_ideo7_mean2021  cz_ideo7_mean2021 ideo7 cz_imm_share
>  female age covid_diagnosed hs college income_50k income_100k city suburb town white black race_hispani
> c asian covid_diagnosed) margin nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps 
> nodepvars 
(output written to TableA11.tex)

. 
. 
end of do-file

. log close
      name:  <unnamed>
       log:  /Users/sungkim/Dropbox/KimPelc5/Vaccination_paper/Final_PB/Replication/vaccine_main.log
  log type:  text
 closed on:   7 Jan 2024, 19:41:40
---------------------------------------------------------------------------------------------------------
